CENTER FOR RESEARCH ON INTELLIGENT PERCEPTION AND COMPUTING
Message from Director
The Center for Research on Intelligent Perception and Computing (CRIPAC) is an independent department at the Institute of Automation, Chinese Academy of Sciences (CASIA). With focuses on three key research platforms, namely SIR (Smart Identity Recognition), DIG (Data Intelligence Gathering) and iSEE (Intelligent Scene Exploration and Evaluation), CRIPAC Biometric Acquisition and Recognition aims to establish intelligent information processing systems that integrate the power of humans, computers and things, and to provide scientific theories, key technologies, pioneering applications and innovative talents for the era of intelligence. The past year 2015 witnessed the flourishing developments of our center. Currently, CRIPAC has an energetic team including four full professors, six associate professors or senior engineers, seven assistant professors, two post-doctoral research fellows, twenty-six technicians and sixty- two postgraduate students. The research fellows in CRIPAC all strive to be the backbone of the science and technology industry in China. Prof. Liang Wang was granted the National Science Fund for Distinguished Young Scholars and he was rated as excellent in the final assessment for CAS Hundred Talent Program, Prof. Kaiqi Huang was elected as Young top-notch talent for Ten Thousand Talent Program and excellent member of CAS Youth Innovation Promotion Association, Associate Prof. Ran He won the Second Prize of Wu Wenjun Artificial Intelligence Science and Technology Innovation Award, ACM Beijing Rising Star Award and member of CAS Youth Innovation Promotion Association, Associate Prof. Yongzhen Huang won the Tencent Rhino Bird Excellent Prize, Prof. Tieniu Tan, Prof. Liang Wang, Prof. Kaiqi Huang, Prof. Zhenan Sun, Associate Prof. Ran He and Associate Prof. Yongzhen Huang were elected as members of CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT). With regard to funding applications, CRIPAC has obtained two more grants, Vision Pattern Analysis and Understanding (National Science Fund for Distinguished Young Scholars) and Brain Functional Connection Mapping and Brain-Like Intelligence Research (CAS Advanced Research Project). CRIPAC members remained productive in 2015. Sixteen journal papers were published. The paper titled “An Adaptive Ensemble Classifier for Steganalysis Based on Dynamic Weighted Fusion” by PhD student Xikai Xu won the Best Paper Award of CSPS 2015. The paper titled “1000 Fps Highly Accurate Eye Detection with Stacked Denoising Autoencoder” by PhD student Wei Tang won the Best student Paper Award of CCCV2015, and the paper titled “Fusion of Face and Iris Biometrics on Mobile Devices Using Near-infrared Images” by PhD student Qi Zhang won the Best Paper Award of CCBR2015. In the realm of key techniques, we have made breakthroughs in face recognition based on deep learning, iris recognition, gait recognition, action recognition, image recognition and image retrieval. When it comes to applying the latest technology, the large-scale crowd surveillance system has been successfully deployed in the frontlines of anti-terrorist and safeguard stability, face recognition system has been used in internet finance and iris recognition system has b een employed for numerous tourist sites. In terms of technology transfer and industrialization, Tianjin Academy for Intelligent Recognition Technologies (TAiFRT) was officially established in Tianjin Binhai New Area. This academy serves as the industrialization platform for research fellows in CRIPAC. TAiFRT aims to make substantial contributions to Beijing-Tianjin- Hebei integration. With respect to industry alliance, the China Biometrics Alliance (CBA) now has a total of 85 member companies. The Big Image and Video Data Alliance (BIDA) has attracted a total of 58 member companies. These alliances will build up a coordinative innovation platform for manufacturing, studying, researching and deploying, and therefore speed up the industrialization process of science and technology outputs in China. CRIPAC is always keen on establishing academic and industrial links, and is eager to collaborate with worldwide leading research institutes, companies, investigators and governments to promote the development of intelligence and its applications for the well-being of humanity. Director of Center for Research on Intelligent Perception and Computing CENTER FOR RESEARCH ON INTELLIGENT PERCEPTION AND COMPUTING
Table of Contents
Introduction 1 Organizational Structure 2 People 3 Faculty 3 Administrative Staff 15 Technical Staff 16 Postdoctors 17 Postgraduates 18 Research Directions 20 Biometrics 20 Image and Video Analysis 20 Big Data and Multi-modal Computing 20 Content Security and Authentication 21 Sensing and Information Acquisition 21 Research Facilities 22 Representative Research Progress 24 Overview of Current Research Projects 27 Publications 33 Awards and Recognitions 39 Professional Activities 40 Academic Exchanges and Cooperations 42 Conferences and Workshops 42 Visits 42 Research Talks by External Visitors 44 Memorabilia in 2015 45 CENTER FOR RESEARCH ON INTELLIGENT PERCEPTION AND COMPUTING
Introduction
The Center for Research on Intelligent Perception and Computing (CRIPAC) is affiliated to the Institute of Automation, Chinese Academy of Sciences (CAS). Focusing on the theory and technology of intelligent perception and computing, the Center aims at conducting cutting-edge research and applications, and strives to build a world-class research organization in terms of personnel training, technological innovation and technology transfer. Currently, the Center’s work mainly concerns information sensing, intelligent recognition, content security, system integration and applications. The Center currently has seventeen faculty members including one CAS academician, two CAS “Hundred Talents Program” recipients, two NSFC Distinguished Youth Fund awardee and one NSFC Excellent Youth Fund winner. These research faculty members are supported by a total of twenty eight administrative and technical staff members, and around seventy over researchers including post-doctoral research fellows, PhD and MSc students, visiting scholars and intern students. The Center has a large number of quality publications including more than 600 journal and conference papers, and more than 90 patents. CRIPAC also has won many prestigious national and international awards in recent years, including the Second Prize of the National Award for Technology Invention, the Second Prize of the National Award for Science & Technology Progress, the Second Prize of the National Award for Natural Science, the First Prize of the Beijing Science and Technology and so on. Moreover, CRIPAC achieves the best performance on several world-leading competitions, e.g., champions of object detection in PASCAL VOC 2010 and 2011, and the first place in a series of NICE competitions.
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Organizational Structure
Beijing Vistek Technology startup Co.,Ltd. companies
Beijing IrisKing Co., Ltd.
Director Deputy Director Tianjin Academy for Intelligent Chief Engineer Recognition Technologies
Image and Video Analysis Group
CRIPAC Biometrics Group
Big Data and Multi-modal Computing Group
Administration General Office Committee
Chinese Biometrics Alliance (CBA)
Alliances Chinese Big Image and video Data Alliance (BIDA)
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People
Faculty Xiaotang Chen Dr. Xiaotang Chen received the BSc degree from Xidian University in 2008. She received the PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences (CASIA) in 2013. In July 2013, Dr. Chen joined the Center for Research on Intelligent Perception and Computing (CRIPAC) as an Assistant Professor. Her major research interests include computer vision and pattern recognition. Dr. Chen is currently a member of IEEE and serves as the program committee member of several conferences. Selected Publications 1. Xiaotang Chen, Kaiqi Huang and Tieniu Tan, “Object Tracking Across Non-Overlapping Views by Learning Inter- Came ra Transfer Models,” Pattern Recognition, vol. 47, no. 3, pp. 1126-1137, 2014. 2. Xiaotang Chen, Kaiqi Huang and Tieniu Tan, “Learning the Three Factors of a Non-Overlapping Multi-Camera Network Topology,” Proc. Chinese Conference on Pattern Recognition, pp. 104-112, September 2012, Beijing, China. 3. Xiaotang Chen, Kaiqi Huang and Tieniu Tan, “Object Tracking Across Non-Overlapping Cameras Using Adaptive Models,” Proc. ACCV Workshop on Detection and Tracking in Challenging Environments, pp. 464-477, November 2012, Korea. 4. Xiaotang Chen, Kaiqi Huang and Tieniu Tan, “Direction-based Stochastic Matching for Pedestrian Recognition in Non-Overlapping Cameras,” Proc. IEEE International Conference on Image Processing, pp. 2065-2068 September 2011, Belgium.
Jing Dong PhD, Associate Professor. Dr. Jing Dong received her BSc in Electronic Information Science and Technology from Central South University in 2005 and her PhD in Pattern Recognition from the Graduate University of Chinese Academy of Sciences. Since July 2010, Dr. Dong has joined the National Laboratory of Pattern Recognition (NLPR), where she is currently an Associate Professor. Her research interests include pattern recognition, image processing and digital image forensics. She has published over 30 academic papers and she is a member of CCF, CAAI, IEEE, IEEE Computer Science Society, Signal processing Society and Communication Society. She also serves as the deputy general of Chinese Association for Artificial Intelligence and an IEEE Volunteer leader in R10 and in Beijing Section from many aspects of academic activities. Selected Publications 1. Zairan Wang, Jing Dong and Wei Wang, “Quantization Based Watermarking Methods Against Valumetric
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Distortions,” [J] International Journal of Automation and Computing, 2015. 2. Wei Wang, Jing Dong and Tienu Tan, “Exploring DCT Coefficient Quantization Effects for Local Tampering Detection,” IEEE Trans. on Information Forensics and Security, vol. 9, no. 10, pp. 1653-1666, 2014. 3. Jing Dong, Wei Wang and Tieniu Tan, “CASIA Image Tampering Detection Evaluation Database,” Proc. IEEE China Summit and International Conference on Signal and Information Processing, pp. 422-426, July 2013, Beijing, China. 4. Jing Dong and Tieniu Tan, “Blind Image Steganalysis Based on Run-length Histogram Analysis,” Proc. IEEE International Conference on Image Processing, pp. 2064- 2067, October 2008, USA. 5. Jing Dong and Tieniu Tan, “Security Enhancement of Biometrics, Cryptography and Data Hiding by Their Combinations,” Proc. 5th International Conference on Visual Information Engineering, pp. 239-244, July, 2008.
Ran He Dr. Ran He received the BSc degree in Computer Science from Dalian University of Technology, the MSc degree in Computer Science from Dalian University of Technology, and PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 2001, 2004 and 2009, respectively. Since September 2010, Dr. He has joined NLPR where he is currently an Associate Professor. He is currently a member of IEEE and serves as an associate editor of Elsevier Neurocomputing (2011- ) and IET Image process (2012). He has widely published at highly ranked international journals, such as IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Trans. on Image Processing (TIP), IEEE Trans. on Knowledge and Data Engineering (TKDE), IEEE Trans. on Neural Network and Learning System (TNNLS), and Neural Computation (NECO), and leading international conferences, such as Computer Vision and Pattern Recognition (CVPR) and the AAAI Conference on Artificial Intelligence (AAAI). Selected Publications 1. Kaiye Wang, Ran He, Liang Wang, Wei Wang, Tieniu Tan, “Joint Feature Selection and Subspace Learning for Cross-modal Retrieval,” IEEE Trans. on Pattern Analysis and Machine Intelligence, preprint, 2016. 2. Ran He, Tieniu Tan and Liang Wang, “Robust Recovery of Corrupted Low-Rank Matrix by Implicit Regularizers,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 4, pp. 770-783, 2014. 3. Ran He, Wei-Shi Zheng, Tieniu Tan and Zhenan Sun, “Half-Quadratic Based Iterative Minimization for Robust Sparse Representation,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 2, pp. 261- 275, 2014. 4. Ran He, Wei-Shi Zheng, Bao-Gang Hu and Xiang-Wei Kong, “Two-stage Nonnegative Sparse Representation for Large-scale Face Recognition,” IEEE Trans. on Neural Network and Learning System (TNNLS), , vol. 24, no. 1, pp. 35-46, 2013. 5. Ran He, Wei-Shi Zheng and Bao-Gang Hu, “Maximum Correntropy Criterion for Robust Face Recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 33, no. 8, pp.1561-1576, 2011.
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Guangqi Hou Dr. Guangqi Hou received his BSc, MSc and PhD degree from Beijing Institute of Technology, China in 2001, 2004 and 2011, respectively. He was a research fellow at the Institute of Automation, Chinese Academy of Sciences from 2011 to 2013. In July 2013, Dr. Hou joined the National Laboratory of Pattern Recognition (NLPR) as an Assistant Professor, and became an Associate Professor since Nov. 2014. His current research focuses on Optical Imaging, Computational Photography, Light Field Photography, Pattern Recognition and Computer Vision. He is a member of IEEE/SPIE, and a reviewer of TIFS/ Neuocomputing/Sensors/IJAC. Selected Publications 1. Chi Zhang, Guangqi Hou, Zhenan Sun and Tieniu Tan, “Efficient Auto-Refocusing of Iris Images for Light-Filed Cameras,” Proc. International Joint Conference on Biometrics, Sep 29-Oct 2, 2014, Florida USA. 2. Jie Gui, Zhenan Sun, Guangqi Hou and Tieniu Tan, “An Optimal Set of Code Words and Correntropy for Rotated Least Squares Regression,” Proc. International Joint Conference on Biometrics, Sep 29-Oct 2, 2014, Florida USA. 3. Shu Zhang, Guangqi Hou and Zhenan Sun, “Eyelash Removal Using Light Field Camera for Iris Recognition,” Proc. Chinese Conference on Biometric Recognition, vol.8833, pp. 319-327, November 2014, Shengyang, China. 4. Chi Zhang, Guangqi Hou, Zhenan Sun, Tieniu Tan and Zhiliang Zhou, “ Light Field Photography for Iris Image Acquisition,” Proc. Chinese Conference on Biometric Recognition, pp. 345-352, November 2013, Jinan, China 5. Guangqi Hou and Ping Wei, “Efficient Stereo Matching Scheme Based on Graph Cuts,” Journal of Beijing Institute of Technology, Vol.4 2010.
Kaiqi Huang Dr. Kaiqi Huang received his BSc and MSc from Nanjing University of Science and Technology, China and obtained his PhD degree from Southeast University. He is a professor in National Laboratory of Pattern Recognition (NLPR). He is an IEEE Senior member and deputy general secretary of IEEE Beijing Section (2006-2008). Dr. Huang has managed successfully many leading-edge research projects, including 4 National Natural Science Foundation of China, 1 National High Technology Research and Development Program ("863"Program) of China (No. 2009AA01Z318), and several international collaborative projects. He has served as a member of the Program Committee for over 40 international conferences; he is an executive team member of IEEE SMC Cognitive Computing Committee as well as Associate Editor of IEEE Trans. on Systems, Man, and Cybernetics: Systems (TSMC), International Journal of Image and Graphics (IJIG), Electronic Letters on Computer Vision and Image Analysis (ELCVIA) and a Guest Editor of Signal Processing special issue on Security. He has obtained some awards including The National Science and Technology Progress Award in 2011, Beijing Science & Technology Star Award and Beijing Science and Technology Progress Award in 2008. His current research interests include visual surveillance, digital image processing, pattern recognition and biological
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based vision and so on. He has published over 100 papers in important international journals and conferences such as IEEE TIPAMI, T-IP, T-SMC-B, TCSVT, Pattern Recognition (PR), Computer Vision and Image Understanding (CVIU), ECCV, CVPR, ICIP and ICPR. He received the Best Student Paper Awards from ACPR10, the winner prizes of the object detection task in both PASCAL VOC'10 and PASCAL VOC'11, the honorable mention prize of the classification task in PASCAL VOC'11, the winner of the classification task (additional data) in ImageNet-ILSVRC 2014. Selected Publications 1. Kaiqi Huang, Chong Wang and Dacheng Tao, “High-OrderTopology Modeling of Visual Words for Image Classification,” IEEE Trans. on ImageProcessing, vol. 24, no.11, pp. 3598-3608, 2015. 2. Kaiqi Huang, Yeying Zhang and Tieniu Tan, “A Discriminative Model of Motion and Cross Ratio for View- Invariant Action Recognition,” IEEE Trans. on Image Processing, vol. 21, no. 4, pp. 2187-2197, 2012. 3. Kaiqi Huang, Dacheng Tao, Yuanyan Tang, Xuelong Li and Tieniu Tan, “Biologically Inspired Features for Scene Classification in Video Surveillance,” IEEE Trans. on Systems, Man and Cybernetics, Part B, vol. 41,no. 1, pp. 307-313, 2011. 4. Kaiqi Huang and Tieniu Tan, “Vs-star: A Visual Interpretation System for Visual Surveillance,” Pattern Recognition Letters, vol. 31, no. 14, pp. 2265-2285, 2010. 5. Kaiqi Huang, Shiquan Wang, Tieniu Tan and Stephen J. Maybank, “Human Behavior Analysis Based on a New Motion Descriptor,” IEEE Trans. on Circuits and Systems for Video Technology, vol.19, no.12, pp. 1830-1840, 2009.
Yongzhen Huang Dr. Yongzhen Huang received his B.E. degree from Huazhong University of Science and Technology (HUST) in 2006, and his PhD. degree from Institute of Automation, Chinese Academy of Sciences (CASIA) in 2011. Then he joined National Laboratory of Pattern Recognition (NLPR) as an Assistant Professor in July 2011, and became an Associated Professor since Nov. 2013. His research interests include computer vision, pattern recognition, machine learning, and computational visual cognition. He has published one book and more than 50 papers in international journals and conferences such as IEEE TPAMI, IEEE TMSC-B, IEEE TCSVT, TMM, Neurocomputing, CVPR, ICCV, NIPS, ICPR, ICIP. He has obtained several honors and awards, including the Excellent Doctoral Thesis of Chinese Association for Artificial Intelligence (2012), the Best Student Paper of Chinese Conference on Computer Vision (2015), the Champion of PASCAL VOC Challenges on object detection (2010 and 2011), the Runner-up of PASCAL VOC Challenges on object classification (2011), and the Champion of Internet Contest for Cloud & Mobile Computing on Human Segmentation with 230,000RMB (2013), the Second Prize and the Prize of Highest Accuracy with Low Energy in LPIRC (Low-Power Image Recognition Challenge) (2015). Dr. Huang is currently a member of IEEE. He has served as Associate Editor of Neurocomputing, the web chair of AVSS2012, the publicity chair of CCPR2012, the program committee member of 6 conferences, and the peer reviewer of over 20 journals and conferences.
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Selected Publications 1. Yongzhen Huang and Tieniu Tan, Feature coding for Image Representation and Recognition, Springer, invited book, 2015. 2. Yongzhen Huang, Zifeng Wu, Liang Wang and Tieniu Tan, “Feature Coding in Image Classification: A Comprehensive Study,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 3, pp. 493- 506, 2014. 3. Yongzhen Huang, Kaiqi Huang, Dacheng Tao, Tieniu Tan and Xuelong Li, “Enhanced Biologically Inspired Model for Object Recognition,” IEEE Trans. on Systems, Man and Cybernetics, Part B, vol. 41, no. 6, pp. 1668-1680, 2011 4. Yongzhen Huang, Kaiqi Huang, Yinan Yu and Tieniu Tan, “Salient Coding for Image Classification,” Proc. International Conference on Computer Vision and Pattern Recognition, pp. 1753-1760, June 2011, USA. 5. Yongzhen Huang, Kaiqi Huang, Chong Wang and Tieniu Tan, “Exploring Relations of Visual Codes for Image Classification,” Proc. International Conference on Computer Vision and Pattern Recognition, pp. 1649-1656, June 2011, USA.
Haiqing Li Dr. Haiqing Li received the B.E. degree in automation from the Sichuan University. He received the PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences (CASIA) in 2009, and 2015, respectively. He is currently an Assistant Professor with the Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), CASIA. His major research interests include biometrics, computer vision, pattern recognition and machine learning. Selected Publications 1. Haiqing Li, Zhenan Sun, Man Zhang, Libin Wang, Lihu Xiao and Tieniu Tan, “A brief survey on recent progress in iris recognition,” Proc. Chinese Conference on Biometric Recognition, vol.8833, pp. 288-300, November 2014, Shenyang, China. 2. Haiqing Li, Zhenan Sun and Tieniu Tan, “Robust Iris Segmentation Based on Learned Boundary Detectors,” Proc. International Conference on Biometrics, pp. 317-322, March 2012, India. 3. Haiqing Li, Zhenan Sun and Tieniu Tan, “Accurate Iris Localization Using Contour Segments,” Proc. International Conference on Pattern Recognition, pp. 3398-3401, November 2012, Japan.
Zhenan Sun Dr. Zhenan Sun received the BSc degree in Industrial Automation from Dalian University of Technology, the MSc degree in Systems Engineering from Huazhong University of Science and Technology, and PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 1999, 2002 and 2006, respectively. Since March 2006, Dr. Sun has joined the National Laboratory of Pattern Recognition (NLPR) where he is
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currently a Professor. His current research focuses on biometric recognition for personal identification, image processing and computer vision. Selected Publications 1. Zhenan Sun, Hui Zhang, Tieniu Tan and Jianyu Wang, “Iris Image Classification Based on Hierarchical Visual Codebook,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) , vol. 36, no. 6, pp. 1120-1133, 2014. 2. Zhenan Sun, Libin Wang and Tieniu Tan, “Ordinal Feature Selection for Iris and Palmprint Recognition,” IEEE Trans. on Image Processing, vol. 23, no. 9, pp. 3922-3934, 2014. 3. Zhenan Sun and Tieniu Tan, “Ordinal Measures for Iris Recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 31, no. 12, pp. 2211-2226, 2009. 4. Zhenan Sun, Tieniu Tan, Yunhong Wang and Stan Z. Li, “Ordinal Palmprint Representation for Personal Identification,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 279-284, June 2005, USA.
Tieniu Tan Dr. Tieniu Tan received his BSc degree in electronic engineering from Xi'an Jiaotong University, China, in 1984, and his MSc and PhD degrees in electronic engineering from Imperial College London, U.K., in 1986 and 1989, respectively. In October 1989, he joined the Computational Vision Group at the Department of Computer Science, The University of Reading, Reading, U.K., where he worked as a Research Fellow, Senior Research Fellow and Lecturer. In January 1998, he returned to China to join the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of the Chinese Academy of Sciences (CAS), Beijing, China, where he is currently a Professor and the director of Center for Research on Intelligent Perception and Computing (CRIPAC), and was former director (1998- 2013) of the NLPR and Director General of the Institute (2000-2007). He is currently also Vice President of the Chinese Academy of Sciences. He has published 13 edited books or monographs and more than 500 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition. His H-index is 66 (as of March 2015). His current research interests include biometrics, image and video understanding, and information content security. Dr. Tan is a Fellow of CAS, TWAS (The World Academy of Sciences for the advancement of science in developing countries), IEEE and IAPR (the International Association of Pattern Recognition), and an International Fellow of the UK Royal Academy of Engineering. He has served as chair or program committee member for many major national and international conferences. He is or has served as Associate Editor or member of editorial boards of many leading international journals including IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition, Pattern Recognition Letters, Image and Vision Computing, etc. He is Editor-in-Chief of the International Journal of Automation and Computing.
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He was founding chair of the IAPR Technical Committee on Biometrics, the IAPR-IEEE International Conference on Biometrics, the IEEE International Workshop on Visual Surveillance and Asian Conference on Pattern Recognition (ACPR). He is currently the Deputy President of Chinese Artificial Intelligence Association. He has served as the President of the IEEE Biometrics Council. He has given invited talks and keynotes at many universities and international conferences, and has received many national and international awards and recognitions. Selected Publications 1. Ran He, Tieniu Tan and Liang Wang, “Robust Recovery of Corrupted Low-Rank Matrix by Implicit Regularizers,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 4, pp. 770-783, 2014. 2. Zhenan Sun, Hui Zhang, Tieniu Tan and Jianyu Wang, “Iris Image Classification Based on Hierarchical Visual Codebook,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) , vol. 36, no. 6, pp. 1120-1133, 2014. 3. Yongzhen Huang, Zifeng Wu, Liang Wang and Tieniu Tan, “Feature Coding in Image Classification: A Comprehensive Study,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 3, pp. 493- 506, 2014. 4. Tieniu Tan, Xiaobo Zhang, Zhenan Sun and Hui Zhang, “Noisy Iris Image Matching by Using Multiple Cues,” Pattern Recognition Letters,vol. 33, no. 8, pp. 970-977, 2012. 5. Tieniu Tan, Zhaofeng He and Zhenan Sun, “Efficient and Robust Segmentation of Noisy Iris Images for Non- cooperative Iris Recognition,” Image and Vision Computing, vol. 28, no. 2, pp. 223-230, 2010.
Liang Wang Dr. Liang Wang received both the B. Eng. and M. Eng. degrees from Anhui University in 1997 and 2000 respectively, and the PhD degree from the Institute of Automation, Chinese Academy of Sciences (CAS) in 2004. From 2004 to 2010, he worked as a Research Assistant at Imperial College London, United Kingdom and Monash University, Australia, a Research Fellow at the University of Melbourne, Australia, and a lecturer at the University of Bath, United Kingdom, respectively. Currently, he is a full Professor of Hundred Talents Program at the National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P. R. China. His major research interests include machine learning, pattern recognition and computer vision. He has widely published at highly-ranked international journals such as IEEE TPAMI and IEEE TIP, and leading international conferences such as CVPR, ICCV and ICDM. He has obtained several honors and awards such as the Special Prize of the Presidential Scholarship of Chinese Academy of Sciences. He is currently a Senior Member of IEEE and a Fellow of IAPR, as well as a member of BMVA. He is an associate editor of IEEE Transactions on Cybernetics, International Journal of Image and Graphics, Neurocomputing and so on. He is a guest editor of 9 special issues, a co-editor of 6 edited books, a co- program chair of 3 conferences, and a co-chair of 12 international workshops. Selected Publications 1. Liang Wang and Chuan Li, “Spectrum-Based Kernel Length Estimation for Gaussian Process Classification,” IEEE
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Trans. on Cybernetics, vol. 44, no.6, pp. 805-816, 2014. 2. Ran He, Tieniu Tan and Liang Wang, “Robust Recovery of Corrupted Low-Rank Matrix by Implicit Regularizers,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 4, pp. 770-783, 2014. 3. Worapan Kusakunniran, Qiang Wu, Hongdong Li and Liang Wang, “Recognizing Gaits Across Views Through Correlated Motion Co-Clustering,” IEEE Trans. on Image Processing, vol. 23, no. 2, pp. 696-709, 2014. 4. Yongzhen Huang, Zifeng Wu, Liang Wang and Tieniu Tan, “Feature Coding in Image Classification: A Comprehensive Study,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 3, pp. 493- 506, 2014. 5. Chen Wang, Junping Zhang, Liang Wang, Jian Pu, and Xiaoru Yuan, “Human Identification Using Temporal Information Preserving Gait Templates,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 34, no. 11,pp. 2164-2176, 2012.
Wei Wang Dr. Wei Wang received the BSc degree in the Department of Automation from Wuhan University in 2005, and the PhD degree in the School of Information Science and Engineering at the Graduate University of Chinese Academy of Sciences (GUCAS) in 2011. Since July 2011, Dr. Wang has joined the National Laboratory of Pattern Recognition (NLPR) where he is currently an Assistant Professor. His research interests focus on computer vision, pattern recognition, visual attention and deep learning. Selected Publications 1. Yan Huang, Wei Wang, Liang Wang, “Bidirectional Recurrent Convolutional Networks for Multi-Frame Super- Resolution,” Proc. Advances in Neural Information Processing Systems, pp. 235-243, December 2015, Quebec, Canada. 2. Wei Wang, Yan Huang, Yizhou Wang and Liang Wang, “Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction,” Proc. IEEE International Conference on Computer Vision and Pattern Recognition Workshops, pp. 496-503, June 2014, Columbus, USA. (Best Paper) 3. Wei Wang, Cheng Chen, Yizhou Wang, Tingting Jiang, Fang Fang and Yuan Yao, “Simulating Human Saccadic Scanpath on Natural Images,” Proc. IEEE International Conference on Computer Vision and Pattern Recognition, pp. 441-448, June 2011, Colorado Springs, USA. 4. Wei Wang, Yizhou Wang, Qingming Huang and Wen Gao, “Measuring Visual Saliency by Site Entropy Rate,” Proc. IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2368-2375, June 2010, San Francisco, USA. 5. Wei Wang, Yizhou Wang, Luoshe Huo, Qingming Huang, and Wen Gao, “Symmetric Segment-Based Stereo Matching for Motion Blurred Images with Illumination Variations,” Proc. International Conference on Pattern Recognition, pp.1-4, December 2008.
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Wei Wang Dr. Wei Wang received his B.E. in Computer Science and Technology from North China Electric Power University in 2007 and his PhD in Pattern Recognition from the Graduate University of Chinese Academy of Sciences. Since July 2012, Dr. Wang has joined the National Laboratory of Pattern Recognition (NLPR) where he is currently an Assistant Professor. His research interests include pattern recognition, image analysis and processing, and digital image forensics. He is a member of CCF, IEEE, IEEE Computer Society, and Signal Processing Society. Selected Publications 1. Wei Wang, Jing Dong and Tienu Tan, “ Exploring DCT Coefficient Quantization Effects for Local Tampering Detection,” IEEE Trans. on Information Forensics and Security, vol. 9, no. 10, pp. 1653-1666, 2014. 2. Wei Wang, Jing Dong and Tieniu Tan, “Exploring DCT Coefficient Quantization Effect for Image Tampering Localization,” Proc. IEEE International Workshop on Information Forensics and Security, pp. 1-6, December 2011, Brazil. 3. Wei Wang, Jing Dong and Tieniu Tan, “Tampered Region Localization of Digital Color Images Based on JPEG Compression Noise,” Proc. International Workshop on Digital Watermarking, pp. 120-133, October 2010, Korea. 4. Wei Wang, Jing Dong and Tieniu Tan, “Image Tampering Detection Based on Stationary Distribution of Markov Chain,” Proc. IEEE International Conference on Image Processing, pp. 2101-2104, September 2010, China. 5. Wei Wang, Jing Dong and Tieniu Tan, “Effective Image Splicing Detection Based On Image Chroma,” Proc. IEEE International Conference on Image Processing, pp. 1257-1269, November 2009, Egypt.
Shu Wu Dr. Shu Wu received the BSc degree from Hunan University, China, in 2004, the MSc degree from Xiamen University, China, in 2007, and the PhD degree from the University of Sherbrooke, Canada, in 2012, all in computer science. He is an Assistant Professor in the Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences. His research interests include data mining, recommendation systems, pervasive computing, and network data analytics. He is a member of the IEEE, ACM and ACM SIGIR. Selected Publications 1. Qiang Liu, Shu Wu and Liang Wang, “COT: Contextual Operating Tensor for Context-aware Recommender Systems,” Proc. AAAI Conference on Artificial Intelligence, pp. 203-209, January 2015, Austin, Texas, USA. 2. Shu Wu and Shengrui Wang, “Information-theoretic Outlier Detection for Large-scale Categorical Data,” IEEE Trans. on Knowledge and Data Engineering (TKDE), VOL. 25, 2013. 3. Shu Wu and Shengrui Wang, “Parameter-free Outlier Detection for Large-scale Categorical Data,” Proc. International Conference on Machine Learning and Data Mining (MLDM 2011), 2011. 4. Shu Wu and Shengrui Wang, “Rating-based Collaborative Filtering Combined with Additional Regularization,” Proc.
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International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2011), 2011. 5. Shu Wu, Qingshan Jiang and Joshua Zhexue Huang, “A New Initialization Method for Clustering Categorical Data,” Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), 2007.
Junge Zhang Dr. Junge Zhang received the BSc degree from China University of Geosciences (CUG) in 2008. He received his PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences (CASIA) in 2013. In July 2013, Dr. Zhang joined the Center for Research on Intelligent Perception and Computing (CRIPAC) as an Assistant Professor. His major research interests include computer vision, pattern recognition. Dr. Zhang is currently a member of IEEE and a committee member of CCF YOCSEF. He served as the publicity chair and the technical program committee member of several conferences, and the peer reviewer of over 10 international journals and conferences. In 2010 and 2011, he and his group members won the champion of PASCAL VOC challenge on object detection and the second place on object classification. Selected Publications 1. Junge Zhang, Yongzhen Huang, Kaiqi Huang, Zifeng Wu and Tieniu Tan, “Data Decomposition and Spatial Mixture Modeling for Part based Model,” Proc. Asian Conference on Computer Vision, vol. 7724, pp. 123-137, November 2012, Korea. 2. Junge Zhang, Xin Zhao, Yongzhen Huang, Kaiqi Huang and Tieniu Tan, “Semantic Windows Mining in Sliding Window Based Object Detection,” Proc. International Conference on Pattern Recognition, pp. 3264-3267, November 2012, Japan. 3. Junge Zhang, Kaiqi Huang, Yinan Yu and Tieniu Tan, “Boosted Local Structured HOG-LBP for Object Localization,” Proc. International Conference on Computer Vision and Pattern Recognition, pp. 1394-1400, June 2011, USA. 4. Junge Zhang, Yinan Yu, Shuai Zheng and Kaiqi Huang, “An Empirical Study of Visual Features for Part Based Model,” Proc. Asian Conference on Pattern Recognition, 2011. 5. Junge Zhang, Yinan Yu, Yongzhen Huang, Chong Wang, Weiqiang Ren, Jinchen Wu, Kaiqi Huang and Tieniu Tan, “Object Detection based on Data Decomposition, Spatial Mixture Modeling and Context,” International Conference on Computer Vision Workshop on Visual Object Classes Challenge, 2011.
Man Zhang Dr. Man Zhang received her BSc degree in Information Engineering from Beijing University of Posts and Telecommunications (BUPT) and PhD degree in Computer Application Technology from the Institute of Automation, Chinese Academy of Sciences (CASIA) in 2008 and 2013, respectively. Since July 2013, Dr. Zhang has joined the Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR) where she is currently an Assistant Professor. Her research interests are image processing, pattern
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recognition and biometrics. She is a member of IEEE. She also has served as an IEEE Volunteer and student delegate in R10. Selected Publications 1. Man Zhang, Jing Liu, Zhenan Sun, et al, “The First ICB Competition on Iris Recognition,” Proc. International Joint Conference on Biometrics, Sep 29- Oct 2, 2014, Florida USA. 2. Man Zhang, Zhenan Sun and Tieniu Tan, “Deformed Iris Recognition Using Bandpass Geometric Features and Lowpass Ordinal Features,” Proc. International Conference on Biometrics, June 2013, Spain. 3. Man Zhang, Zhenan Sun and Tieniu Tan, “Perturbation-enhanced Feature Correlation Filter for Robust Iris Recognition,” IET Biometrics, vol. 1, no. 1, pp. 37-45, 2012. 4. Man Zhang, Zhenan Sun and Tieniu Tan, “Deformable DAISY Matcher for Robust Iris Recognition,” Proc. IEEE International Conference on Image Processing, pp. 3250-3253, September 2011, Belgium.
Zhang Zhang Dr. Zhang Zhang received the BSc degree in Computer Science and Engineering from Hebei University of Technology and the PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 2002 and 2009, respectively. From 2009 to 2010, he was a research fellow at Nanyang Technological University, Singapore. In September 2010, Dr. Zhang joined the National Laboratory of Pattern Recognition (NLPR) where he is currently an Associate Professor. His current research focuses on pattern recognition, intelligent visual surveillance, human action and activity recognition. He is a member of the IEEE. Selected Publications 1. Zhang Zhang and Dacheng Tao, “Slow Feature Analysis for Action Recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol.34, no. 3, pp. 436-450, 2012. 2. Zhang Zhang, Tieniu Tan and Kaiqi Huang, “An Extended Grammar System for Learning and Recognizing Complex Visual Events,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 33, no. 2, pp. 240-255, 2011. 3. Zhang Zhang, Kaiqi Huang and Tieniu Tan, “Multi-Thread Parsing for Recognizing Complex Events in Videos,” Proc. European Conference on Computer Vision, pp. 738-751, October 2008, France. 4. Zhang Zhang, Kaiqi Huang, Tieniu Tan and Liangsheng Wang, “Trajectory Series Analysis based Event Rule Induction for Visual Surveillance,” Proc. IEEE International Conference of Computer Vision and Pattern Recognition, pp.1-8, June 2007, USA.
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Xin Zhao Dr. Xin Zhao received his BSc degree from Anhui University of Technology in 2006 and his PhD degree in Pattern Recognition and Intelligent System from the University of Science and Technology of China in 2013. Since July 2013, Dr. Zhao has joined the National Laboratory of Pattern Recognition (NLPR) as an Assistant Professor. His research interests are computer vision, pattern recognition and data mining. He has published several academic papers in the international conferences. He is a member of CCF, IEEE, and IEEE Signal Processing Society. He won the Best Student Paper Award in the First Asian Conference on Pattern Recognition in 2011. Selected Publications 1. Xin Zhao, Yinan Yu, Yongzhen Huang, Kaiqi Huang and Tieniu Tan, “Feature Coding via Vector Difference for Image Classification,” Proc. International Conference on Image Processing, October 2012, USA. 2. Xin Zhao, Kaiqi Huang and Tieniu Tan, “A Comparison Study on Kernel Based Online Learning for Moving Object Classification,” Proc. Chinese Conference on Intelligent Visual Surveillance, pp. 17-20, December 2011, Beijing, China. 3. Xin Zhao, Jianwei Ding, Kaiqi Huang and Tieniu Tan, “Global and Local Training for Moving Object Classification in Surveillance-Oriented Scene,” Proc. Asian Conference on Pattern Recognition, pp. 681-685, November 2011, China. 4. Xin Zhao, Weiqiang Ren, Kaiqi Huang and Tieniu Tan, “Online Codebook Reweighting Using Pairwise Constraints for Image Classification,” Proc. Asian Conference on Pattern Recognition, pp. 662-666, November 2011, China.
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Administrative Staff Guohua Chen is currently an Lixia Ma is currently the Director administrative staff member of of the General Office of CRIPAC. CRIPAC. He is in charge of the She received her BSc degree general affairs of the Chinese from Renmin University of China Biometrics Alliance(CBA), (RUC) in 2005. She has joined including publicity, document the National Laboratory of Pattern management of alliance members, Recognition (NLPR), Institute activity organization, etc. of Automation of the Chinese Academy of Sciences, where she is currently a senior Ruie Guo is currently an engineer. She is in charge of document management, administrative staff member of human resources, publicity, academic activity CRIPAC. She received her BSc arrangement, regulations, editing, safety and so on. degree from Beijing University of Posts and Telecommunications Liping Yin is currently an (BUPT) in 2009. She is in charge administrative staff member of of the center achievements CRIPAC. She received her BSc (awards/ patents/ publications) management, annual degree from Beijing University and monthly reports, device purchase and management, of Technology in 2008. She is in network maintenance and technical support, conference charge of research projects and organization, reimbursement and other general affairs. fund management, document management, conference organization, salary, expenditure Chen Huang is currently an and reimbursement, and other general affairs. administrative staff member of CRIPAC. He is in charge of the general affairs of the Chinese Big Image and Video Data Alliance (BIDA), including publicity, document management of alliance members, and activity organization.
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Technical Staff
Ping Bai Di Cao Honglei Gao Zhaofeng He Zhichao Jiang Assistant Engineer Assistant Engineer Assistant Engineer Algorithm Engineer Algorithm Engineer
Yunfeng Kang Xingguang Li Yaobo Li Jing Liu Yong Liu Engineer Algorithm Engineer Software Engineer Algorithm Engineer Assistant Engineer
Pengcheng Ma Yanan Qin Jianwen Wang Jingqiu Wang Lei Wang Algorithm Engineer Engineer Software Engineer Assistant Engineer Assistant Engineer
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Xinhao Wang Zhen Wang Yue Wu Yuantao Xie Ping Yang Software Engineer Software Engineer Hardware Engineer Software Engineer Assistant Engineer
Wei Yin Xu Zhang Hardware Engineer Software Engineer
Postdoctors
Jie Gui Yunlian Sun
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Postgraduates
PhD Candidates
Year of Name Advisor Research Area Enrollment Di Miao 2010 Tieniu Tan Score level fusion of multibiometrics Dong Wang 2010 Tieniu Tan Link prediction and analysis for social networks Qi Li 2011 Tieniu Tan A study on facial landmark detection and face clustering Kangwei Liu 2011 Kaiqi Huang Visual structure models for object detection and recognization Yinlong Qian 2011 Tieniu Tan Feature learning for setganalysis via deep models Lijun Cao 2012 Kaiqi Huang Wide area visual group analysis for real scenes Yanhua Cheng 2012 Kaiqi Huang Large scale visual recognition WeiYu Guo 2012 Tieniu Tan Modeling interests and behaviors of users in social media Yan Huang 2012 Liang Wang Deep learning and cyber data understanding Yueying Kao 2012 Kaiqi Huang Aesthetics in computer vision Jinde Liu 2012 Tieniu Tan Behavior analysis in video Nianfeng Liu 2012 Tieniu Tan Biometric recognition based on computational imaging Qiyue Yin 2012 Liang Wang Research on multi-view data analysis and applications Shu Zhang 2012 Tieniu Tan Representation Learning for Video based Face Recognition Zhen Zhou 2012 Tieniu Tan Context-constrained Visual Data Analysis and Applications Muhammad Rauf 2013 Liang Wang Deep Learning Dong Cao 2013 Tieniu Tan Face recognition and attribute analysis in dynamic scenes Yong Du 2013 Liang Wang Deep representation learning for action recognition Human pose estimation based on structured representation and Lianrui Fu 2013 Kaiqi Huang learning Zhen Jia 2013 Tieniu Tan Research on visual semantic understanding Fei Liu 2013 Tieniu Tan Research on depth map enhancement based on light field camera Hongsong Wang 2013 Liang Wang Deep learning in visual computing Wei Tang 2013 Liang Wang Large-scale visual computing and its applications Dangwei Li 2013 Kaiqi Huang Multi-camera tracking Peng Bo 2013 Tieniu Tan Research on passive multimedia forensics Qiang Liu 2013 Liang Wang Multimedia data mining of the webs Jian Liang 2013 Tieniu Tan Localization on visual targets Jun Tian 2013 Tieniu Tan Short text classification research based on deep neural networks Qiang Cui 2013 Liang Wang Mining from multi-Model and multi-view data Deepak kumar Jain 2014 Kaiqi Huang Human behavior analysis Yong Gwon Ri 2014 Tieniu Tan Digital image forensics Weihua Chen 2014 Kaiqi Huang Pedestrian identification in multi-camera scenes Jingyu Liu 2014 Liang Wang Theory and method of mobile vision
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Fusion of face and iris biometrics on mobile devices using near- Qi Zhang 2014 Tieniu Tan infrared images Junbo Wang 2014 Tieniu Tan Attention-driven memory networks for computer vision Chunshui Cao 2014 Tieniu Tan Research of feedback neuro network Yunbo Wang 2015 Zhenan Sun HCI based on light field imaging Qiaozhe Li 2015 Kaiqi Huang Key techniques of visual analysis Da Li 2015 Tieniu Tan Large scale visual feature learning based on slow feature analysis
MSc Candidates
Year of Name Advisor Tentative Dissertation Title Enrollment Lingxiao Song 2013 Zhenan Sun Human-computer interaction based on light-field images Ran Xu 2013 Kaiqi Huang Research on image super resolution Weiqi Zhao 2013 Kaiqi Huang Action recognition in video Wenlong Cheng 2014 Liang Wang Data mining for multimedia content security Jiedong Hao 2014 Tieniu Tan Network image forensics Yabei Li 2014 Tieniu Tan Learning object detector from videos Yan Li 2014 Kaiqi Huang large scale object recognition Yuqi Zhang 2014 Liang Wang Theory and application of deep learning Lingxiao He 2014 Zhenan Sun Research on deep learning based iris recognition Wenzhen Huang 2014 Kaiqi Huang Machine learning in computer vision Xiaoxiang Liu 2014 Tieniu Tan Heterogeneous biometric recognition Yunlong Wang 2015 Tieniu Tan Intelligent interaction and perception based on light-field image analysis Weining Wang 2015 Liang Wang Cross-scene and cross-view gait recognition Jiabin Ma 2015 Liang Wang Brain-like visual computing and applications Hongwen Zhang 2015 Zhenan Sun Research and applications of face image preprocessing Houjing Huang 2015 Kaiqi Huang Object detection and tracking in visual surveillance Huikai Wu 2015 Kaiqi Huang Visual cognition and object recognition Yibo Hu 2015 Ran He Image segmentation Yali Wang 2015 Zhenan Sun Personal identification based on behavioral features on mobile devices
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Research Directions
Biometrics Biometrics group consists of 2 professors, 1 associate professor, 2 assistant professors, 2 post-doctors, 5 hired engineering and project management staff members, 11 PhD and MSc students. The group undertakes projects from to National Basic Research Program of China, National Sci. & Tech. Support Program, Instrument Developing Project of the Chinese Academy of Sciences, National Natural Science Foundation of China and other research projects. It aims to combine the imaging techniques and identification algorithms to enhance the users’ capacity adaptable to the environment in the multi-modal biometric recognition system, and to accomplish iris and face recognition from the controlled scenes to complex scenes. The group will insist on the innovation of obtaining the features of face and iris under complex scenes. Furthermore, the group will follow the technology roadmap: “imaging devices - data resources - recognition algorithms - integrated systems - practical applications”, and provide high-level identification technology for public safety. Till now, the group published more than 100 international journal and conference articles and has more than 20 granted patents. Besides, the group was awarded the 2nd Prize of National Technological Invention. Furthermore, the biometric group has released the largest iris dataset in the world and the dataset is used by more than 10,000 research units from in more than 120 countries.
Image and Video Analysis Image and video analysis refers to automatically analyzing objects and high-level semantic information from large- scale images and videos based on the techniques of pattern recognition, artificial intelligence and computer vision. The major research directions include: (1) Research on object detection and recognition in images and videos. Against the background of big data, we research the problem of hierarchical and structural visual representation in large-scale object detection and recognition to improve their performance in real applications. (2) Research on object tracking and behavior analysis in wide-area and complex situations. To serve the needs of public security, semantic information of human actions and behaviors in visual surveillance is studied to realize the abnormal behavior detection and analysis. (3) Research on pedestrian attribute analysis in complex camera networks. This group focuses on pedestrian attribute analysis and re-identification in non-overlapping camera networks by exploiting the relationships across cameras. The group has published many papers in top international conferences and journals, with the second prize of National Scientific and Technological Progress in 2011. In the PASCAL VOC challenge, the group won the first prize of object detection in 2010 and 2011. The group also attended the ImageNet Classification and Localization with additional training data Challenge and won the first prize of image classification in 2014.
Big Data and Multi-modal Computing The big data and multi-modal computing research group deals with different types of data, such as images, text, videos,
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etc. It studies both theory and applications about pattern recognition, visual computing, machine learning, data mining, context modeling, etc. The main achievements include the champion of 2013 Internet Contest for Cloud & Mobile Computing in human segmentation, the best student paper award of ICPR2014, etc. Major research interests of the multi-modality computing group include: (1) Visual computing based on non-Euclidean space and large scale and multi- modality data analysis. It studies the computational theories and mechanisms on topology-based object representations, unsupervised clustering, cross-modality data analysis and large scale machine learning. (2) Methods and applications for visual computing based on deep learning. It studies how to efficiently integrate the feedback mechanism in feedforward networks and how to combine active vision in feedforward and feedback networks, which can be used to solve many vision tasks in large scale vision analysis, e.g., object recognition, object detection, video segmentation and video analysis. (3) Intelligent data analysis for public security and business intelligence. It studies the key technology of large scale social network data mining taking advantage of advanced technologies of big data, such as context modeling, time-series prediction and user modeling, which adapts to the needs of public and content securities.
Content Security and Authentication The group focuses on cyber content security and authentication, by analyzing the integrality and authenticity for the image content, based on pattern recognition and statistical analysis tools. The research of cyber content security is very important for information forensics and security. The research topic includes: (1) Research on the statistic feature model of image forensics. (2) Feature learning based image forensics. (3) Image tempering detection and localization based on pattern analysis. (4) Source classification and device linking and (5) Research on prototype systems of image forensics.
Sensing and Information Acquisition The sensing and information acquisition group will focus on the innovation of intelligent sensing theory, methods and equipment by the cross-fusion of artificial intelligence, cognitive science, optics, electronics and control engineering, and provide the novel technology for National public security and information industry development. The group aims to perceive and describe multi-modal scenes information precisely and achieve accurate reconstruction of the complex scenes. Till now, the group focuses on the research on multi-modal biometric acquisition device, which can capture iris, face and gait biometrics at a distance in the unconstrained environment. Such research achievement will be useful for biometrics research and public security applications. Key technologies include light field computational imaging, intelligent human-computer interaction, multi-biometric data preprocessing, coding, fusion, visualization and so on.
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Research Facilities
1. DIG (Data Intelligence Gathering) platform DIG deals with big multi-modal data. It is an integrated system including data gathering, storing, managing, computing and exhibiting based on Hadoop. It plans to explore the full potential value of big data and improve the ability of analyzing data. The functions of DIG are described as follows. (1) Gather, store and manage large scale multi-modal heterogeneous data and provide various interfaces for data access. (2) Integrate many important algorithms in computer vision, pattern recognition, machine learning and data mining in order to build a general data analysis platform. (3) Integrate typical achievements in various scientific research areas and provide corresponding demos. (4) Provide open and complete solutions for intelligent data analysis by taking full advantage of CRIPAC.
2. iSEE (Intelligent Scene Exploration and Evaluation) platform Taking advantage of the development of big data and related data analysis technology, iSEE integrates multi-scene video data, large scale parallel processing architecture and intelligent video processing algorithms. Main functions of iSEE are summarized as follows. (1) Mass data gathering in video surveillance. (2) Algorithm evaluation for large scale perception technologies. (3) Large scale data mining for mass video data and large scale data retrieval for perception metadata. (4) Multi-field knowledge integration and multi-dimension of visualization.
3. SIR (Smart Identity Recognition) platform SIR aims at exploring collaborative innovation by combining technologies of photoelectric imaging, algorithms of image processing and theories of pattern recognition to provide human-computer interfaces with good user experience, obtain high quality iris and face images in complex scenarios, and implement accurate, and efficient identification. It contains three major components. (1) An integration experimental platform developed by ourselves, which computes the optimal computational imaging model for multi-modal biometrics in complex scenarios. (2) A large scale biometrics database, which is the fundamental for algorithm developments, efficiency evaluation, and standard establishment of biometrics. (3) A library of core algorithms for biometrics, including image pre-processing, feature extraction, feature selection, feature comparison, bioassay, and fast retrieval, secure encryption algorithms, etc.
4. Light-field camera Raytrix is a world-famous manufacture of lenselet-based light-field camera. Raytrix can extend the depth of field up to
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6 times compared to conventional cameras via applying the well-arranged micro-lens array with multiple focal lengths. CRIPAC equips two types of Raytrix camera now, include: R5: It has an approximately 5 Mega-pixels CMOS image sensor. The highest resolution of the effective image is close to 1 Mega-pixels (25%). It can extend the depth of range up to 6 times in contrast to conventional cameras. It can be connected to the upper computer via the GigE interface and the maximum frame rate is up to 15 fps. It can render refocused images at 50 levels of distinguishable depths. R25: It has an approximately 25 Mega-pixels CCD image sensor. The highest resolution of the effective image is close to 5 Mega-pixels (25%). It can extend the depth of range up to 6 times comparing to conventional cameras. It can be connected to the upper computer via the Camera-Link interface and the maximum frame rate is up to 25 fps. It can render refocused images at 100 levels of distinguishable depths.
R5 R25
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Representative Research Progress
On Combining Multiple Instance Linear SVM and Bag Splitting for High Performance Visual Object Localization Localizing objects of interest in images when provided with only image-level labels is a challenging visual recognition task. Previous efforts have required carefully designed features and have difficulty in handling images with cluttered backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications. In this paper, we propose an efficient and effective learning framework called MILinear, which is able to learn an object localization model from large-scale data without using bounding box annotations. We integrate rich general prior knowledge into a learning model using a large pre-trained convolutional network. Moreover, to reduce ambiguity in positive images, we present a bag-splitting algorithm that iteratively generates new negative bags from positive ones. We evaluate the proposed approach on the challenging Pascal VOC 2007 dataset, and our method outperforms other state-of-the-art methods by a large margin; some results are even comparable to fully supervised models trained with bounding box annotations. To further demonstrate scalability, we also present detection results on the ILSVRC 2013 detection dataset, and our method outperforms supervised DPM without using box annotations. This work has been accepted by IEEE T-PAMI.
High-Order Topology Modeling of Visual Words for Image Classification Modeling relationship between visual words in feature encoding is important in image classification. Recent methods consider this relationship in either image or feature space, and most of them incorporate only pairwise relationship (between visual words). However, in situations involving large variability in images, one cannot capture intrinsic invariance of intra-class images using low-order pairwise relationship. The result is not robust to larger variations in images. In addition, as the number of potential pairings grows exponentially with the number of visual words, the task of learning becomes computationally expensive. To overcome these two limitations, we propose an efficient classification framework that exploits high-order topology of visual words in the feature space, as follows. First, we propose a search algorithm that seeks dependence between the visual words. This dependence is used to construct higher order topology in the feature space. Then, the local features are encoded according to this higher order topology to improve the image classification. Experiments involving four common data sets, namely PASCAL VOC 2007, 15 Scenes, Caltech 101, and UIUC Sport Event, demonstrate that the dependence search significantly improves the efficiency of higher order topological construction, and consequently increases the image classification in all these data sets. This work has been accepted by IEEE T-IP.
Deep Recurrent Neural Networks for Visual Analysis Recurrent neural networks have a strong ability of modelling the long-term contextual information of temporal sequences, which are mainly used to model one-dimensional sequences before, e.g. speech and language. Recently,
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we make deep research on how to model two-dimensional visual sequences with recurrent neural networks. First, aiming to model the spatial-temporal dependency of visual sequence and reduce the computational cost of the recurrent connections, we propose a recurrent convolutional network which replaces the commonly-used full recurrent connection with a weight-sharing convolutional connection. This model has been successfully used in video super-resolution, and this work is published on Neural Information System Processing (NIPS2015). Second, considering human physical structure and motion characteristics, we propose an end-to-end hierarchical recurrent neural network for skeleton based action recognition. This method has high accuracy and low computational cost, and this work is published on Computer Vision and Pattern Recognition (CVPR2015).
Research on Contextual Information Modeling Contextual information modeling has wide application in data mining, user behavior modeling, and has drawn great attention in research area. We have done thoroughly research on contextual information modeling, which achieves progress in different aspects and meaningful results. First, we use contextual operating matrices to model contextual effects on subjects and objects of behaviors. Meanwhile, for reducing parameter amount, we use replace contextual matrices with a general contextual operating tensor and contextual latent vectors. Our model achieves state-of-the- art performance in context-aware recommendation. Then, we further extend our model, and apply tensor operating calculation for modeling multi-entity interaction, which performs well in recommendation, information retrieval and click prediction. Furthermore, we incorporate convolutional neural networks in modeling contextual behaviors. According to the data characteristics, we apply one-dimensional convolution and one-dimensional pooling. Our model achieves state-of-the-art performance in click-through-rate prediction. Among our works, one has been accepted by AAAI 2015, and two papers have been accepted by CIKM 2015, which brings extensive influence.
Representative Vector Machines: A Unified Framework for Classical Classifiers Classifier design is a fundamental problem in pattern recognition. A variety of pattern classification methods such as the nearest neighbor (NN) classifier, support vector machine (SVM), and sparse representation-based classification (SRC) have been proposed in the literature. These typical and widely used classifiers were originally developed from different theory or application motivations and they are conventionally treated as independent and specific solutions for pattern classification. This paper proposes a novel pattern classification framework, namely, representative vector machines (or RVMs for short). The basic idea of RVMs is to assign the class label of a test example according to its nearest representative vector. The contributions of RVMs are twofold. On one hand, the proposed RVMs establish a unified framework of classical classifiers because NN, SVM, and SRC can be interpreted as the special cases of RVMs with different definitions of representative vectors. Thus, the underlying relationship among a number of classical classifiers is revealed for better understanding of pattern classification. On the other hand, novel and advanced classifiers are inspired in the framework of RVMs
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Structured Ordinal Measure: Theory and Methods In biometrics, ordinal measure is defined as the relative ordering of some property - for example, the average brightness of two adjacent regions or the relative ordering of two color channels within the same region. We further present a structured ordinal measure method for biometric recognition that simultaneously learns ordinal filters and structured ordinal features. The problem is posed as a non-convex integer program problem that includes two parts. The first part learns stable ordinal filters to project video data into a large-margin ordinal space. The second seeks self-correcting and discrete codes by balancing the projected data and a rank-one ordinal matrix in a structured low-rank way. Unsupervised and supervised structures are considered for the ordinal matrix. In addition, as a complement to hierarchical structures, deep feature representations are integrated into structured ordinal measure to enhance coding stability. An alternating minimization method is employed to handle the discrete and low-rank constraints, yielding high-quality codes that capture prior structures well. Experimental results on three commonly used face video databases show that our method with a simple voting classifier can achieve state-of-the-art recognition rates using fewer features and samples.
Image Steganalysis Based on Deep Learning An image steganlaysis scheme based on deep learning is proposed. Current steganlaysis techniques are mostly based on heuristic feature extraction and surpervised learning. However, facing the big data era, these methods can achive good detection results on specific and limited testing samples. Our research focuses on unsurpervised learning steganalysis using deep learning. The steganlaysis feature can be extracted on the image neighborhood structure by the meantime for final classification. We also design the deep learning algorithms and proper networks for final classification.
Image Steganalysis Based on Local Learning Cover-Source Mismatch Problem is the most seriours problem for keeping iamge steganalysis from real application. Facing the big data era, the exsisting steganalysis methods can achive good detection results on specific and limited testing samples but no good for untrained or unseen samples. Our research focuses on supervised steganalysis based on local learning, which can make best use of the relationships between cover and its corrisponding stego image pairs. The steganlaysis feature can be extracted on the image neighborhood structure by the meantime for final classification. We also tested our algorithms for some real application and achieve better results compared to the state-of-arts.
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Overview of Current Research Projects
Social Sensing Data Processing for Public Security Sources of Funding: National Fundamental Research Program We can obtain massive data from both physical and cyber space of our society. For example, the CCTV cameras can capture the real-time video data related to surveillance scenes, objects, human, cars, etc. And the social media such as website, blog, micro-blog, BBS provides the thoughts and feelings of people. The social sensing data in both physical space and cyber space is closely related to the public security. So the project aims to extract useful security-related information from the social sensing data. The main research topics include cross-camera events understanding, cross- media data mining of social media, and cross-space social sensing data processing.
Public Platform and Verification System for Perception Data Processing Source of Funding: National Fundamental Research Program The effective processing and semantic understanding of complex perceptual data provide essential technical support for the real-time monitoring and emergency response with respect to cyber security. This project targets on the social perceptual data in both physical and cyber space including large-scale visual data, multimodal biometric data and large- scale cyber unstructured data. To develop fundamental theories and specific solutions to analyze social perceptual data, we need to: (1) address the perception mechanism and computational methods for the complex perceptual data; (2) understand cross-scene visual data; (3) analyze cross-media cyber data; (4) collaboratively analyze data crossing the physical and cyber space. Through these techniques the social perceptual data can be better simplified and integrated. Also more understandable information can be utilized to forecast and respond many emergencies concerning the public security.
Dynamic Scene Understanding across Cameras Sources of Funding: National Fundamental Research Program Generally, research on object detection, object tracking, behavior analysis and recognition is under single cameras. However, single cameras have a limited field of views, and occlusion is always a challenging problem, greatly influencing the system’s performance. Thus, multiple-cameras cooperate to compose a network to monitor large area, which can partly solve the problem of occlusions. In fact, for the sake of economic cost and computational cost, non- overlapping multi-camera tracking systems are widely used. Thus, solving the problem of non-overlapping multi- camera object tracking is of great significance. The aim of multi-camera tracking is to establish correspondence between observations of objects across cameras. In this project, we will focus on the object matching and recognition across cameras, topology recovering, data association, and color transformations across cameras.
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Digital Content Service based on Copyright Protection Sources of Funding: National Key Technology Support Program Digital copyright protection has gained more and more attention with a wide customer group in recent years. Digital content service based on copyright protection is seemed as a useful solution for the service industry. The lack of optimized copyright protection technology is a key issue for digital content service industry. Currently, watermarking, encryption and digital signature authentication are commonly used in this area. Our research focused on watermarking algorithm, copyright protection system and efficient data retrieval design etc.
Advanced Devices for Acquisition of Multi-modal Biometric Images under Unconstrained Environment Sources of Funding: NSFC This project aims to develop an advanced device to capture iris, face and gait biometrics at a distance under unconstrained environment. Such a device will be useful for biometrics research, identification industry and public security applications. The joint interdisciplinary research team has good experiences in pattern recognition, computer vision, photoelectrical engineering, precision instruments, and integrated circuit. The device combines light field imaging, depth perception and multispectral imaging to actively capture multi-modal biometric image sequences. Extremely high resolution image sensors are developed from IC combination of multiple CCD or CMOS chips. Embedded computing system will be developed for online processing, encoding, fusion and enhancement of image data. This project will develop the first iris, face and gait image/video database under unconstrained environment and we can use it to develop novel biometric methods and technology.
Object Detection and Classification in Image based on Visual Cognition Sources of Funding: NSFC for the Excellent Young Scholars Object detection and classification is a fundamental and still unsolved problem. It has wide application in visual surveillance, robot navigation and complex video understanding. The key problem in this field is how to get the topological description of the object. Although significant progress has been achieved in this topic, it still cannot meet people’s ultimate expectation or goal. In this project, we will address this problem from three aspects: (1) building novel local structured feature descriptor. (2) Topologically modeling objects with local structured descriptors. (3) Reducing model’s complexity for accelerating training and testing. Finally, we will construct a system which can be easily applied for semantic video understanding.
Visual Computing Based on Non-Euclidean Space Sources of Funding: NSFC Computer vision makes machines to perceive circumstance with intelligence. It is important in both theoretical researches and practical applications. Most current computer vision algorithms are based on Marr’s vision theory. It
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lacks robustness dealing with complex conditions and is limited to work in the Euclidean space, which is different from the human vision system. This project originates from the study to human’s visual perception. Its major research issues include: Mathematical basis and theoretical framework of non-Euclidean space; Visual computing and pattern recognition of non-Euclidean space; Fusion of machine and human visual systems to explore cognitive mechanism of non-Euclidean space; Applications of non-Euclidean space in image and video analysis, object detection and recognition. This project attempts to change the basis of computer vision, i.e., the space it works in. It is promising to create a new direction of computer vision and to provide theories for practical applications such as multimedia retrieve, video surveillance and biometrics.
Steganalysis Sources of Funding: NSFC, 115 Research Fund Information hiding (Steganography) has been a hot topic and has drawn much attention in recent years. However, cases have been reported where Steganography has been abused for bad purposes. Hence, the research of steganalysis, which is a counter-technology of steganography aimed at detecting the presence of secret message in cover medium, serves the urgent needs of network security to block covert communication with illegal information. The project focuses on the following six parts. 1) Fundamental theory in security of information hiding and in Steganalysis; 2) Study on the specific stegnography algorithms; 3) Research of pattern recognition based steganalysis approach; 4) Research on the statistic feature model of image steganalsysis; 5) Fusion and optimization of steganalysis feature and classifier. 6) Research on a prototype system of steganalysis which integrates the results of above research contents. The results of this project will enrich the theory of stegnography and steganalysis, and can give the essential foundation of methods and techniques for reliable application of steganalysis to make a contribution for network security and national security.
Research on Large-Scale Visual Computing Theories and Methods Sources of Funding: NSFC Visual computing is a combination of computer vision, machine learning, pattern recognition and other related fields. Due to the importance of ubiquitous visual data and promising applications, large-scale image / video processing and understanding have attracted strong interest of researchers. Although in recent years there have been some important progress, many challenging research issues remain unresolved, e.g., the limited description capacity of the existing visual features; in terms of visual representation, large-scale multi-source heterogeneous data lack effective analysis tools; the traditional classifier for large-scale, multi-class high-dimensional data is inefficient in visual learning and reasoning. For a major demand of China's information industry for advanced visual computing technologies, and for international cutting-edge research directions in the vision field, this project aims at the joint of outstanding vision researchers from China and Australia to study large-scale visual computing theories and methods, to breakthrough a major bottleneck in the efficiency, accuracy, robustness in terms of current visual computing technologies, and
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to develop visual feature extraction, representation, learning and reasoning methods for large-scale datasets, while providing the theoretical basis and key technical support for a wide range of promising applications such as wide-area video monitoring, multi-modal biometrics, and Internet mass visual information security.
Multi-modal Learning via Structured Constraints Sources of Funding: NSFC The integration and analysis of multi-modal data has drawn much attention in the pattern recognition and computer vision communities. It is still a challenging and ongoing issue because the data from different modalities have different semantic representation ability. This project makes use of the text-image pairs in web pages as an example of multi- modal data, and focuses on the structure prior behind multi-modal data. We aim to propose new multi-modal learning theory and methods via structure prior. For the multi-modal theory, based on information theoretic learning and implicit regularizers, we study the mathematic formulation of structure prior, and the measurement of correlation between multi-modal data. In particular, we study structured sparsity and low-rank matrix constraints based on implicit regularizers, and develop a half-quadratic framework for both of them. For multi-modal methods, we study the properties of the low-dimensional subspace of original high-dimensional multi-modal data, and analyze the influence of different subspace structures for multi-modal learning. And taking structure constraints as regularization terms, we further study coupled feature selection, coupled hashing and coupled clustering for multi-modal data. Finally, we study the integration and analysis technique for multi- modal data.
Scalable Slow Feature Learning for Intelligent Visual Surveillance Sources of Funding: NSFC Effective feature representation is very important for developing intelligent visual surveillance applications. Current hand-crafted features, e.g., Histogram of Oriented Gradients (HoG), has achieved superior performance in pedestrian detection and tracking. However, it is still a hard problem to adapt the features to the variances in surveillance scenes. In this project, we will focus on the scalable representation learning for intelligent visual surveillance. Firstly, we will collect a large scale moving objects dataset in long-term surveillance scenes by common motion detection and tracking methods. Then, based on slow feature analysis (SFA) which can learn invariant features from sequential data, we will develop a scalable bottom-to-up representation learning method which is guided by the environment constraints in surveillance scenes to obtain multi-layers slow features of moving objects in surveillance scenes. Finally, according to different surveillance tasks, we will further optimize the generic slow features to promote the performance and efficiency of the recognition algorithms. Through the research in this project, we will develop a whole set of scalable slow feature learning methods for visual surveillance. These methods are highly valuable for designing the adaptive recognition algorithms to handle the variances in surveillance scenes, so that the great progress in intelligent visual surveillance can be achieved.
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Hierarchical Visual Attention Model Based on Deep Learning Sources of Funding: NSFC Modelling visual attention is an interdiscipline research topic which invloves in computer science, psychophysiology and computational neuroscience. Current visual attention models mainly focus on modelling the center-surround mechanism of the simple cells in primary visual cortex. However, large amounts of experiments show that selective attention mechanism exists in each layer of visual cortex, and different visual features play different roles in selective attention. In this project, we will propose a deep learning-based hierarchical visual attention model which not only simulates the selective attention of each visual layer with its internal driving mechanism, but also learns multi-layer visual features by combining bottom-up data and top-down task, and learns the importances of the features in each layer. For one thing, the proposed model will improve the accuracy of fixation prediction, which has a large amount of potential applications; for another, embedding attention mechanism into a deep learning framework will improve its performances in several other visual tasks, such as object detection and object recognition.
Intelligent Light-field Imaging for Long-range Iris Recognition of Multiple Subjects Sources of Funding: NSFC Iris imaging is the first essential step of iris recognition. The limitations of current iris imaging in terms of the imaging distance, depth of field, posture and the number of users prevent wide applications of iris recognition technology. Therefore we propose a novel iris imaging method based on light-field imaging and intelligent vision algorithms to provide user-friendly iris image acquisition at a distance. Firstly light-field data is captured by the microlens array and multi-sensors optical system, and then computer vision methods for human-computer interaction such as head pose estimation, face detection and eye detection are integrated with image processing methods such as image stitching and digital refocusing to achieve large volume iris image acquisition of multiple subjects at a distance. The innovations of this project will establish an intelligent light-field imaging method for long-range iris recognition of multiple subjects and promote the development of iris recognition and opto-electonic engineering technology so that long-range iris recognition systems can be widely used in public security applications.
Content based Digital Image Forensics Sources of Funding: 115 Research Fund, NLPR Traditionally, a photograph implies the truth of what has happened. However, in today’s digital age, sometimes seeing is no longer believing, since our modern life is full of digital images and (maliciously) tampering these digital images is easy and simple by using digital processing tools which are widely available (e.g., Photoshop). Many tampered images emerge in news items, scientific experiments and even legal evidences. Therefore, we cannot take the authenticities of images for granted any more. Image tampering detection is a branch of image forensics which aims at assessing the authenticity and the origin of images. The tasks of image forensics can be divided into the following six categories: source classification, device linking, processing history recovery, forgery detection and anomaly investigation. As image tampering detection is just at its infancy stage, there is still much work to be done and some ideas can be borrowed from
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other research areas. Also, knowledge from computer vision, signal processing, computer graphics, pattern recognition and imaging will be needed for further analysis. Our research purpose focuses on 1) Research on natural image statistic model; 2) Learning based image splicing detection; 3) Image tempering detection and location; and 4) source classification and device linking.
Abnormal Activity Analysis for Visual Surveillance Sources of Funding: National Fundamental Research Program With the development of computer vision, activity analysis attracts more and more attention. Nowadays, we are surrounded by cameras because of their low cost and high quality. It is an important application for public security and home security to employ a computer vision method to detect and analyze abnormal activities, such as fighting, robbing in public places, burglary. This project develops an abnormal activity analysis system facing to public places and family scenes. This system will make alarming automatically when some violence, such as fighting and robbing, occur in public places or some abnormal activity, such as falling down and burglary, happen in a family scene.
Light Field Camera for Advanced Computer Vision Sources of Funding: Chinese Academy of Sciences Traditional cameras transform 3D scenes into 2D images so computer vision has become an ill-posed problem. So it is better to provide more visual information to support advanced computer vision. This project proposes to develop a novel camera to capture 4D light field information of visual scenes. Microlens array is used to record the direction information of light fields so that it is possible to reconstruct depth information of visual objects. In addition, image refocusing algorithms are developed to extend the depth of field of cameras. The innovations of light field cameras will significantly facilitate the research of computer vision. The main applications of light field cameras include iris and face recognition at a distance, visual surveillance, etc.
Human Segmentation for Mobile Scene Understanding Sources of Funding: Global Research Outreach Program of SAMSUNG (GRO) Human segmentation is an important step in scene understanding. In mobile scene understanding, human is probably the most important object because most scene understanding tasks are related with humans or serve for humans. In the past decades, the research of human segmentation is not so satisfying, i.e., not sufficiently accurate for practical applications. In this project, we propose to explore highly accurate human segmentation based on deep learning techniques. Our initial algorithm on a challenging human segmentation competition achieves amazing results (nearly 87% segmentation accuracy), largely outperforming all other competitors. This approach has a potential to be a killer application of cognitive scene understanding for mobile devices.
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Publications
International Journals 1. Qiyue Yin, Shu Wu, Ran He and Liang Wang, “Multi-view Clustering via Pairwise Sparse Subspace Representation,” Neurocomputing, vol. 156, pp.12-21, 2015. 2. Junge Zhang, Yanhu Shan and Kaiqi Huang, “ISEE Smart Home (ISH): Smart Video Analysis for Home Security,” Neurocomputing, vol. 149, pp.752-766, 2015. 3. Jianwei Ding, Yunqi Tang, Wei Liu, Yongzhen Huang and Kaiqi Huang, “Tracking by Local Structural Manifold Learning In A New SSIR Particle Filter,” Neurocomputing, vol. 161, pp.277-289, 2015. 4. Chong Wang and Kaiqi Huang, “How to Use Bag-of-words Model Better for Image Classification,” Image and Visual Computing, vol. 38, pp.65-74, 2015. 5. Chong Wang and Kaiqi Huang, “VFM: Visual Feedback Model for Robust Object Recognition,” Journal of Computer Science and Technology, vol. 30, no. 2, pp. 325-339, 2015. 6. Lijun Cao, Xu Zhang, Weiqiang Ren and Kaiqi Huang, “Large Scale Crowd Analysis Based on Convolutional Neural Network,” Pattern Recognition, vol. 48, no.10, pp. 3016-3024, 2015. 7. Yanhu Shan, Zhang Zhang, Peipei Yang and Kaiqi Huang, “Adaptive Slice Representation for Human Action Classification,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 25, no.10, pp. 1624-1636, 2015. 8. Zifeng Wu, Yongzhen Huang and Liang Wang, “Learning Representative Deep Features for Image Set Analysis,” IEEE Trans. on Multimedia, vol. 17, no.11, pp. 1960-1968, 2015. 9. Yan Huang, Wei Wang and Liang Wang, “Unconstrained Multimodal Multi-Label Learning,” IEEE Trans. on Multimedia, vol. 17, no.11, pp. 1923-1935, 2015. 10. Chong Wang, Kaiqi Huang, Weiqiang Ren, Junge Zhang and Stephen J. Maybank, “Large-Scale Weakly Supervised Object Localization via Latent Category Learning,” IEEE Trans. on Image Processing, vol. 24, no.4, pp. 1371-1385, 2015. 11. Kaiqi Huang, Chong Wang and Dacheng Tao, “High-Order Topology Modeling of Visual Words for Image Classification,” IEEE Trans. on Image Processing, vol. 24, no.11, pp. 3598-3608, 2015. 12. Ran He, Yingya Zhang, Zhenan Sun and Qiyue Yin, “Robust Subspace Clustering with Complex Noise,” IEEE Trans. on Image Processing, vol. 24, no.11, pp. 4001-4013, 2015. 13. Ran He, Man Zhang, Liang Wang, Ye Ji and Qiyue Yin, “Cross-Modal Subspace Learning via Pairwise Constraints,” IEEE Trans. on Image Processing, vol. 24, no.12, pp. 5543-5556, 2015. 14. Shu Zhang, Jian Liang, Ran He and Zhenan Sun, “Code Consistent Hashing based on Information-theoretic Criterion,” IEEE Trans. on Big Data, vol. 1, No. 3, pp. 84-94, 2015. 15. Yanhua Cheng, Xin Zhao, Kaiqi Huang and Tieniu Tan, “Semi-supervised Learning and Feature Evaluation for RGB-D Object Recognition,” Computer Vision and Image Understanding, vol. 139, pp. 149-160, 2015. 16. Ran He, Yinghao Cai, Tieniu Tan and Larry S. Davis, “Learning Predictable Binary Codes for Face Indexing,”
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Pattern Recognition, vol. 48, No. 10, pp. 3160-3168, 2015.
International Conferences 1. Qiang Liu, Shu Wu and Liang Wang, “COT: Contextual Operating Tensor for Context-aware Recommender Systems,” Proc. AAAI Conference on Artificial Intelligence, pp. 203-209, January 2015, Austin, USA. 2. Yong Du, Wei Wang and Liang Wang, “Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110-1118, June 2015, Boston, USA. 3. Kangwei Liu, Junge Zhang, Peipei Yang and Kaiqi Huang, “GRSA: Generalized Range Swap Algorithm for the Efficient Optimization of MRFs,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1761- 1769, June 2015, Boston, USA. 4. Fang Zhao, Yongzhen Huang, Liang Wang and Tieniu Tan, “Deep Semantic Ranking Based Hashing for Multi- Label Image Retrieval,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1556-1564, June 2015, Boston USA. 5. Kaiye Wang, Wei Wang and Liang Wang, “Learning Unified Sparse Representations for Multi-modal Data,” Proc. IEEE International Conference on Image Processing, pp. 3545-3549, September 2015, Quebec, Canada. 6. Yueying Kao, Chong Wang and Kaiqi Huang, “Visual Aesthetic Quality Assessment with a Regression Model,” Proc. IEEE International Conference on Image Processing, pp. 1583-1587, September 2015, Quebec, Canada. 7. Jingyu Liu, Yongzhen Huang, Xiaojiang Peng and Liang Wang, “Multi-view Descriptor Mining via Codeword Net for Action Recognition,” Proc. IEEE International Conference on Image Processing, pp. 793-797, September 2015, Quebec, Canada. 8. Lianrui Fu, Junge Zhang and Kaiqi Huang, “Context Aware Model for Articulated Human Pose Estimation,” Proc. IEEE International Conference on Image Processing, pp. 991-995, September 2015, Quebec, Canada. 9. Yuqi Zhang, Wei Wang and Liang Wang, “Scene Text Recognition with Deeper Convolutional Neural Networks,” Proc. IEEE International Conference on Image Processing, pp. 2384-2388, September 2015, Quebec, Canada. 10. Qiyue Yin, Shu Wu and Liang Wang, “Partially Tagged Image Clustering,” Proc. IEEE International Conference on Image Processing, pp. 4012-4016, September 2015, Quebec, Canada. 11. Jinde Liu, Xin Zhao, Kaiqi Huang and Tieniu Tan, “Learning Occlusion Patterns Using Semantic Phrases for Object Detection,” Proc. IEEE International Conference on Image Processing, pp. 686-690, September 2015, Quebec, Canada. 12. Xikai Xu, Jing Dong, Wei Wang and Tieniu Tan, “Robust Steganalysis Based on Training Set Construction and Ensemble Classifiers Weighting,” Proc. IEEE International Conference on Image Processing, pp. 1498-1502, September 2015, Quebec, Canada. 13. Fei Liu, Guangqi Hou, Zhenan Sun and Tieniu Tan, “Albedo Assisted High-Quality Shape Recovery from 4D Light Fields,” Proc. IEEE International Conference on Image Processing, pp. 1220-1224, September 2015, Quebec, Canada.
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14. Xikai Xu, Jing Dong, Wei Wang and Tieniu Tan, “Local Correlation Pattern for Image Steganalysis,” Proc. IEEE China Summit and International Conference on Signal and Information Processing, pp. 468-472, July 2015, Chengdu, China. 15. Bo Peng, Wei Wang, Jing Dong and Tieniu Tan, “Detection of Computer Generated Faces in Videos Based on Pulse Signal,” Proc. IEEE China Summit and International Conference on Signal and Information Processing, pp. 841- 845, July 2015, Chengdu, China. 16. Kaiye Wang, Wei Wang, Liang Wang and Ran He, “A Two-step Approach to Cross-modal Hashing,” Proc. ACM International Conference on Multimedia Retrieval (ICMR, Short Paper), pp. 459-462, June 2015, Shanghai, China. 17. Song Xu, Shu Wu and Liang Wang, “Personalized Semantic Ranking for Collaborative Recommendation,” Proc. ACM SIGIR Conference on Research and Development in Information Retrieval pp. 971-974, August 2015, Santiago, Chile. 18. Qiang Liu, Shu Wu and Liang Wang, “Collaborative Prediction for Multi-entity Interaction with Hierarchical Representation,” Proc. ACM International Conference on Information and Knowledge Management, pp. 613-622, October 2015, Melbourne, Australia. 19. Qiang Liu, Feng Yu, Shu Wu and Liang Wang, “A Convolutional Click Prediction Model,” Proc. ACM International Conference on Information and Knowledge Management, pp. 1743-1746, October 2015, Melbourne, Australia. 20. Qiyue Yin, Shu Wu and Liang Wang, “Incomplete Multi-view Clustering via Subspace Learning,” Proc. ACM International Conference on Information and Knowledge Management, pp. 383-392, October 2015, Melbourne, Australia. 21. Weiyu Guo, Shu Wu, Liang Wang and Tieniu Tan, “Social-Relational Topic Model for Social Networks,” Proc. ACM International Conference on Information and Knowledge Management, pp. 1731-1734, October 2015, Melbourne, Australia. 22. Dong Wang, Qiyue Yin, Ran He, Liang Wang and Tieniu Tan, “Multi-view Clustering via Structured Low-Rank Representation,” Proc. ACM International Conference on Information and Knowledge Management, pp. 1911-1914, October 2015, Melbourne, Australia. 23. Di Miao, Man Zhang, Haiqing Li, Zhenan Sun and Tieniu Tan, “Bin-based Weak Classifier Fusion of Iris and Face Biometrics,” Proc. IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 1-6, September 2015, Washington, USA. 24. Yasser Chacon-Cabrera, Man Zhang, Eduardo Garea Llano and Zhenan Sun, “Iris Texture Description Using Ordinal Co-occurrence Matrix Features,” Proc. Iberoamerican Congress on Pattern Recognition, pp. 184-191, November 2015, Montevideo, Uruguay. 25. Lingxiao Song, Man Zhang, Zhenan Sun, Jian Liang and Ran He, “Two-Step Greedy Subspace Clustering,” Proc. Pacific-Rim Conference on Multimedia, pp. 45-54, September 2015, Gwangju, Korea. 26. Feng Liu, Yongzhen Huang, Wankou Yang and Changyin Sun, “High-level Spatial Modeling in Convolutional Neural Network with Application to Pedestrian Detection,” Proc. IEEE Canadian Conference on Electrical and Computer Engineering, pp. 778-783, May 2015, Halifax, Nova Scotia, Canada.
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27. Qiyue Yin, Shu Wu and Liang Wang, “Learning to Hash for Recommendation with Tensor Data,” Proc. Asia Pacific Web Conference, pp. 292-303, September 2015, Guangzhou, China. 28. Weiyu Guo, Shu Wu, Liang Wang and Tieniu Tan, “Multiple Attribute Aware Personalized Ranking,” Proc. Asia Pacific Web Conference, pp. 244-255, September 2015, Guangzhou, China. 29. Yanhua Cheng, Rui Cai, Xi Zhao and Kaiqi Huang, “Convolutional Fisher Kernels for RGB-D Object Recognition,” Proc. International Conference on 3D Vision, pp. 135-143, October 2015, France. 30. Yung-Hsiang Lu, Alan M. Kadin, Alexander C. Berg, Thomas M. Conte, Erik P. DeBenedictis, Rachit Garg, Ganesh Gingade, Bichlien Hoang, Yongzhen Huang, Boxun Li, Jingyu Liu, Wei Liu, Huizi Mao, Junran Peng, Tianqi Tang, Elie K. Track, Jingqiu Wang, Tao Wang, Yu Wang, Jun Yao, “Rebooting Computing and Low-Power Image Recognition Challenge,” Proc. International Conference on Computer Aided Design, pp. 927-932, November 2015, Austin, America. 31. Chi Zhang, Zhiwei Li, Yanhua Cheng, Rui Cai, Yanghong Chao and Yong Rui, “MeshStereo: A Global Stereo Model with Mesh Alignment Regularization for View Interpolation,” Proc. IEEE International Conference on Computer Vision, pp. 2057-2065, December 2015, Santiago, Chile. 32. Yanhua Cheng, Rui Cai, Chi Zhang, Zhiwei Li, Xin Zhao, Kaiqi Huang and Yong Rui, “Query Adaptive Similarity Measure for RGB-D Object Recognition,” Proc. International Conference on Computer Vision, pp. 145-153, December 2015, Santiago, Chile. 33. Chunshui Cao, Xianming Liu, Jiang Wang, Yinan Yu, Wei Xu, Yi Yang, Deva Ramanan, Chang Huang, Zilei Wang, Thomas Huang, Yongzhen Huang, and Liang Wang, “Look and Think Twice: Capturing Top-down Visual Attention With Feedback Convolutional Neural Networks,” Proc. International Conference on Computer Vision, pp. 2956- 2964, December 2015, Santiago, Chile. 34. Yan Huang, Wei Wang and Liang Wang, “Conditional High-order Boltzmann Machine: A Supervised Learning Model for Relation Learning,” Proc. IEEE International Conference on Computer Vision, pp. 4265-4273, December 2015, Santiago, Chile. 35. Lianrui Fu, Junge Zhang and Kaiqi Huang, “Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation,” Proc. IEEE International Conference on Computer Vision, pp. 1976-1984, December 2015, Santiago, Chile. 36. Lianrui Fu, Junge Zhang and Kaiqi Huang, “Mirrored Non-Maximum Sup-pression for Accurate Object Part Localization,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 37. Dangwei Li, Xiaotang Chen and Kaiqi Huang, “Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 38. Chunfeng Song, Yongzhen Huang, Zhenyu Wang and Liang Wang, “1000fps Human Segmentation with Deep Convolutional Neural Networks,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 39. Kaihao Zhang, Yongzhen Huang, Hong Wu and Liang Wang, “Facial Smile Detection Based on Deep Learning
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Features,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 40. Hongsong Wang, Wei Wang and Liang Wang, “Hierarchical Motion Evolution for Action Recognition,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 41. Yong Du, Yun Fu and Liang Wang, “Skeleton Based Action Recognition with Convolutional Neural Network,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 42. Lingxiao Song, Man Zhang, Qi Li, Zhenan Sun and Ran He, “Float Greedy-search-based Subspace Clustering,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia . 43. Pengcheng Liu, Peipei Yang, Kaiqi Huang and Tieniu Tan, “Uniform Low-Rank Representation for Unsupervised Visual Domain Adaptation,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 44. Dong Wang, Ran He, Liang Wang and Tieniu Tan, “Adaptive Multi-view Clustering via Cross Trace Lasso,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 45. Dong Cao, Ran He, Zhenan Sun and Tieniu Tan, “Joint Space Learning for Video-based Face Recognition,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia . 46. Shu Zhang, Man Zhang, Qi Li, Tieniu Tan and Ran He, “Supervised Topology Preserving Hashing,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia. 47. Jian Liang, Dong Cao, Ran He, Zhenan Sun and Tieniu Tan, “Principal Affinity based Cross-Modal Retrieval,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia . 48. Yan Huang, Wei Wang, Liang Wang, “Bidirectional Recurrent Convolutional Networks for Multi-Frame Super- Resolution,” Proc. Advances in Neural Information Processing Systems, pp. 235-243, December 2015, Quebec, Canada. 49. Dong Cao, Ran He, Man Zhang, Zhenan Sun and Tieniu Tan, “Real-world Gender Recognition Using Multi-Order LBP and Localized Multi-boost Learning,” Proc. IEEE International Conference on Identity, Security and Behavior Analysis, pp. 1-6, March 2015, Hong Kong. 50. Weiyu Guo, Shu Wu, Liang Wang and Tieniu Tan, “Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback,” Proc. IEEE/WIC International Conference on Web Intelligence, December 2015, Singapore. 51. Bo Peng, Wei Wang, Jing Dong and Tieniu Tan, “Improved 3D Lighting Environment Estimation for Image Forgery Detection,” Proc. IEEE International Workshop on Information Forensics and Security, November 2015, Rome, Italy. 52. Kaihao Zhang, Yongzhen Huang, Hong Wu and Liang Wang, “Kinship Verification with Deep Convolutional Neural Networks,” Proc. British Machine Vision Conference, September 2015, Swansea, UK. 53. Pengcheng Liu, Chong Wang, Peipei Yang, Kaiqi Huang and Tieniu Tan, “Cross-Domain Object Recognition Using Object Alignment,” Proc. British Machine Vision Conference (BMVC), September 2015, Swansea, UK. 54. Guangqi Hou, Fei Liu and Zhenan Sun, “Computer Vision Research with New Imaging Technology,” Proc. SPIE International Symposium on Multispectral Image Processing and Pattern Recognition, October 2015, Enshi, China.
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55. Xiang Wang, Lin Li and Guangqi Hou, “Super-Resolved All-Refocused Image with A Plenoptic Camera,” Proc. SPIE International Symposium on Multispectral Image Processing and Pattern Recognition, October 2015, Enshi, China. 56. Xikai Xu, Jing Dong, Wei Wang and Tieniu Tan, “An Adaptive Ensemble Classifier for Steganalysis Based on Dynamic Weighted Fusion,” Proc. International Conference on Communications, Signal Processing, and Systems (CSPS), October 2015, Chengdu, China. 57. Yinlong Qian, Jing Dong, Wei Wang and Tieniu Tan, “Learning Representations for Steganalysis from Regularized CNN Model with Auxiliary Tasks,” Proc. SPIE Media Watermarking, Security, and Forensics, October 2015, Chengdu, China. 58. Yinlong Qian, Jing Dong, Wei Wang and Tieniu Tan, “Deep Learning for Steganalysis Via Convolutional Neural Networks,” Proc. SPIE Media Watermarking, Security, and Forensics, pp. 94090J-1- 94090J-10, March 2015, San Francisco, United States.
National Conferences 1. Wei Tang, Yongzhen Huang and Liang Wang, “1000 fps Highly Accurate Eye Detection with Stacked Denoising Autoencoder,” Proc. Chinese Conference on Computer Vision, pp. 237-246, September 2015, Chengdu, China. 2. Qi Zhang, Haiqing Li, Man Zhang, Zhaofeng He, Zhenan Sun and Tieniu Tan, “Fusion of Face and Iris Biometrics on Mobile Devices Using Near-infrared Images,” Proc. Chinese Conference on Biometric Recognition, pp. 569- 578, November 2015, Tianjin, China.
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Awards and Recognitions
Young top-notch talent for Ten Thousand Talent Program, 2015: Kaiqi Huang Excellent member of CAS Youth Innovation Promotion Association, 2015: Kaiqi Huang National Science Fund for Distinguished Young Scholars, 2015: Liang Wang Excellent in the final assessment for CAS Hundred Talent Program, 2015: Liang Wang Best student paper award in CCCV2015: Wei Tang (advisor: Liang Wang) Best paper award in CCBR2015: Qi Zhang (advisor: Tieniu Tan) Best paper award in CSPS 2015: Xikai Xu (advisor: Tieniu Tan) Member of CAS Youth Innovation Promotion Association, 2015: Ran He Second Prize of Wu Wenjun Artificial Intelligence Science and Technology Innovation Award, 2015: Ran He ACM Beijing rising star award, 2015: Ran He Tencent Rhino Bird Excellent Prize, 2015: Yongzhen Huang Second Prize of Low Power Image Recognition Challenge and Prize of Highest Accuracy with Low Energy in Design Automation Conference, 2015: Yongzhen Huang Champion of “Digital Cup” Basketball League of Institute of Automation, Chinese Academy of Sciences, 2015: Center for Research on Intelligent Perception and Computing (CRIPAC)
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Professional Activities
Jing Dong Secretary General Deputy of Chinese Association for Artificial Intelligence (2014-now) Member of CCF Computer Vision Task Forces (2014-now) Council Member, Beijing Society of Image and Graphics (2012-now) Chair of Student Activities Committee of IEEE Beijing Section (2010-now) IEEE MGA Committee member (2016)
Ran He Member of Editorial Board, IET image processing (2012-) Council Member, Beijing Society of Image and Graphics (2012-) Member of Editorial Board, Neurocomputing ( 2011-)
Kaiqi Huang Guest Ediotr of IEEE Trans on Systems, Man, and Cybernetics: Systems Special issue on Large Scale Video Analytics for Enhanced Security: Algorithms and Systems, 2015 Co-Chair of the International Workshop on Visual Surveillance and Re-Rdentification (Vs-ReID) conjuncted with European Conference on Computer Vision (ECCV2014) Sep.12, 2014. Associate Editor of IEEE Trans on Systems, Man, and Cybernetics: Systems (TSMC) (2014-) Associate Editor of Electronic Letters on Computer Vision and Image Analysis (ELCVIA) (2010-) Board Member, IEEE SMC Technical Committee on Cognitive Computing (2008-) Associate Editor of International Journal of Image and Graphics (IJIG) (2007-)
Yongzhen Huang Associate Editor, Neurocomputing, 2014 -2016, ISSN: 0925-2312
Zhenan Sun Secretary General of the Pattern Recognition Committee of Chinese Association for Artificial Intelligence(CAAI) (2014-) Editorial Board of IEEE Transactions on Information Forensics and Security (2013 -) Secretary General of the Chinese Biometrics Alliance(CBA) (2012 -) Editorial Board of IEEE Biometrics Compendium (2012 -) Secretary General, IAPR Technical Committee on Biometrics (2006 -)
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Tieniu Tan President of Beijing Society of Image and Graphics (2012-) Deputy President of the Chinese Association for Artificial Intelligence (2010 -) Member of Editorial Board, IET Image Processing(2008 -) Member of Advisory Board, Journal of Real-Time Image Processing(2006 -) Editor-in-Chief , Inter. J. Automation and Computing (2004 -)
Liang Wang Associate editor of IEEE Transactions on Information Forensics and Security (IEEE TIFS) (2013 -) Associate editor, IEEE Transactions on Cybernetics (IEEE TSMC-B) (2007 -)
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Academic Exchanges and Cooperations
Conferences and Workshops 1. 2nd Forum on Big Image and Video Data organized by CRIPAC was held in Beijing on April 7. 2. The Brain-like Intelligence Innovation forum organized by Tianjin Academy for Intelligent Recognition Technologies (TAfIRT) was held in Tianjin on June 27. 3. The 2015 Face Recognition and Application Symposium organized by CRIPAC was held in Institute of Automation, Chinese Academy of Sciences on August 7. 4. 3rd Forum on Big Image and Video Data organized by CRIPAC was held in Beijing on October 20. 5. 4th Forum on Chinese Biometrics Industry was held in Shenzhen on October 30. It was hosted by the Chinese Biometrics Alliance (CBA). 6. 10th Chinese Conference on Biometric Recognition (CCBR2015) was held in Tianjin from November 13 to 15 in 2015. It was hosted by the Tianjin Academy for Intelligent Recognition Technologies (TAfIRT) and Chinese Biometrics Alliance (CBA).
Visits External Visits Qiang Liu January 24-30, 2015, AAAI Conference on Artificial Intellegence (AAAI2015) in Austin, USA. Kaiqi Huang From Feb 6, 2015 to Feb 5, 2016, visiting researcher in Oxford University, supported by China Scholarship Council. Man Zhang March 23-25, 2015, IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015) in Hong Kong. Wei Wang, Yong Du June 6-13, 2015, the 28th IEEE International Conference on Computer Vision and Pattern Recognition (CVPR2015) in Boston, USA. Zhenan Sun June 21–26, 2015, the 12th Biometrics Summer School in Alghero, Italy. Di Miao September 7-12, 2015, the 7th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS2015) in Washington D.C., USA. Jing Dong, Fei Liu, Lianrui Fu September 26 - October 2, 2015, the 22nd International Conference on Image Processing (ICIP2015) in Quebec
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City, Canada. Shu Wu, Qiyue Yin October 19-23, 2015, ACM International Conference on Information and Knowledge Management (CIKM2015) in Melbourne, Australia. Bo Peng November 15-20, 2015, the 7th International Workshop on Information Forensics and Security (WIFS2015) in Rome, Italy. Weiyu Guo December 6-9, 2015, IEEE/WIC/ACM International Conference on Web Intelligence (WI2015), Singapore. Tieniu Tan November 03-04, 2015, the 3rd Asian Conference on Pattern Recognition (ACPR2015) in Kuala Lumpur, Malaysia. Liang wang, Lingxiao Song, Hongsong Wang November 02-07, 2015, the 3rd Asian Conference on Pattern Recognition (ACPR2015) in Kuala Lumpur, Malaysia. Wei Wang, Yan Huang December 6-14, 2015, Advances in Neural Information Processing Systems (NIPS2015) in Quebec, Canada. Yanhua Cheng, Chunshui Cao December 9-20, 2015, IEEE International Conference on Computer Vision (ICCV2015) in Santiago, Chile.
Visits to CRIPAC From May to August in 2015, Prof. Edel Bartolo Garcia Reyes from Advanced Technologies Application Center of Cuba visited CRIPAC. From July 1 to July 30 in 2015, Associate Prof. Rocio Gonzalez-Diaz from University of Seville visited CRIPAC.
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Research Talks by External Visitors
Time Speaker Title Postdoctoral Researcher Xiaojiang Peng March 5 Institut national de recherche en informatique Video-Based Human Action Analysis et en automatique Associate Prof. Mingming Cheng March 13 Efficient Image Scene Analysis and Applications Nankai University Lead Researcher Jingdong Wang April 10 Big Media Data: Search and Management Microsoft Research Asia Associate Prof. Zhuowen Tu Deep Supervision for Deep Learning: Training, July 7 University of California Regularization, and Multi-Scale Learning Prof. Yi Zeng Brain-inspired Intelligence: Opportunities, Progresses, July 30 Institute of Automation, CAS and Grand Challenges Associate Prof. Yongjin Liu Cognitive Mechanism Related to Line Drawings and Its July 30 Tsinghua University Applications in Intelligent Processing of Visual Media Prof. Rongrong Ni July 30 Recent Advances in Digital Image Forensics Beijing Jiaotong University Lead Researcher Jingdong Wang July 30 Salient Object Detection and Its Applications Microsoft Research Asia Prof. Peng Cui July 30 Social-sensed Multimedia Computing Tsinghua University Prof. Xinshan Zhu July 30 Small Unmanned Aerial Vehicle Theory and Applications Tianjin University
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Memorabilia in 2015
1. 2nd Forum on Big Image and Video Data organized by CRIPAC was successfully held in Beijing on April 7. More than 300 people from government, industry, academia, investment and media were present. 2. Tianjin Academy for Intelligent Recognition Technologies (TAfIRT) was officially established on April 29. Members in CRIPAC will jointly push the developments of applying intelligent recognition technologies to identity authentication, video surveillance, human computer interaction, information retrieval. 3. On June 17, Prof. Tieniu Tan was invited to give a talk on “the present and future for Artificial Intelligence” during the 8th group study of the 12th Standing committee of the National Committee of the CPPCC. After the talk, Prof. Tieniu Tan had a discussion with the committee members. 4. The Brain-like Intelligence Innovation forum organized by Tianjin Academy for Intelligent Recognition Technologies (TAfIRT) was held in Tianjin on June 27. More than 200 experts and representatives from both home and abroad attended the forum. 5. On July 8, our basketball team won the “Digital Cup” Basketball League of Institute of Automation, Chinese Academy of Sciences, 2015. 6. Summer Symposium of CRIPAC was successfully held from July 17 to July 19 in 2015. 7. On August 7, The 2015 Face Recognition and Application Symposium organized by CRIPAC was held in Institute of Automation, Chinese Academy of Sciences. More than 200 people were present. 8. CRIPAC organized an Orientation Day for new students On September 14. 9. 3rd Forum on Big Image and Video Data organized by CRIPAC was successfully held in Beijing on October 20. More than 300 people from government, industry, and academia were present. 10. 4th Forum on Chinese Biometrics Industry organized by Chinese Biometrics Alliance (CBA) was held in Shenzhen on October 30. More than 200 people were present. 11. 10th Chinese Conference on Biometric Recognition (CCBR2015) was held in Tianjin from November 13 to 15 in 2015. It was hosted by the Tianjin Academy for Intelligent Recognition Technologies (TAfIRT) and Chinese Biometrics Alliance (CBA). More than 280 people were present. 12. The annual meeting of the 973 project led by Prof. Tieniu Tan, was held on December 27. 13. The annual Winter Symposium of CRIPAC was successfully held from December 26 to December 28 in 2015.
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