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Block-Level Discrete Cosine Transform Coefficients for Autonomic Face Recognition Willie L

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Block-Level Discrete Cosine Transform Coefficients for Autonomic Face Recognition Willie L Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2003 Block-level discrete cosine transform coefficients for autonomic face recognition Willie L. Scott, II Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Engineering Science and Materials Commons Recommended Citation Scott, II, Willie L., "Block-level discrete cosine transform coefficients for autonomic face recognition" (2003). LSU Doctoral Dissertations. 4059. https://digitalcommons.lsu.edu/gradschool_dissertations/4059 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. BLOCK-LEVEL DISCRETE COSINE TRANSFORM COEFFICIENTS FOR AUTONOMIC FACE RECOGNITION A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Interdepartmental Program in Engineering Science by Willie L. Scott, II B.S., Louisiana State University, 1998 M.S., Louisiana State University, 2001 May 2003 ACKNOWLEDGEMENTS First and foremost, I would like to thank God for blessing me to complete my research; without His divine help, I would have never made it this far. I would like to express my sincere appreciation and thanks to my advisor and major professor, Dr. Subhash Kak, for his constant guidance and valuable comments, without which, this dissertation would not have been successfully completed. My gratitude also goes out to Dr. Jianhua Chen, Dr. Gerald Knapp, and Dr. Suresh Rai, for serving on my defense committee, and special thanks to Dr. Donald Kraft for his willingness to lend a hand. I also wish to thank the members of my family, my caring mom, my encouraging dad, and my cool little brother, for always being there for me and motivating me to stay focused. Last but certainly not least, I want to thank Michele for always being very supportive and reminding me that everything would turn out fine. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS.................................................................................... ii LIST OF TABLES................................................................................................. vi LIST OF FIGURES .............................................................................................. vii ABSTRACT........................................................................................................... ix CHAPTER 1. INTRODUCTION ...........................................................................1 1.1 Network of Networks Model ......................................................................1 1.2 Hierarchical Processing of Face Images in the Human Vision System.......7 1.2.1 The Human Capacity for Face Recognition........................................8 1.2.2 Spatial Vision......................................................................................9 1.2.3 The Contrast Sensitivity Function ......................................................9 1.2.4 Channels............................................................................................11 1.2.5 Interaction of Channels in Human Vision ........................................16 1.3 Image Transforms ......................................................................................18 1.3.1 Discrete Fourier Transform...............................................................21 1.3.2 Karhunen-Loeve Transform..............................................................22 1.3.3 The Discrete Cosine Transform........................................................24 1.3.3.1 One-dimensional DCT.........................................................27 1.3.3.2 Two-dimensional DCT ........................................................28 1.3.3.3 JPEG Compression ..............................................................29 1.3.3.4 MPEG Compression ............................................................31 1.4 Summary....................................................................................................32 CHAPTER 2. FACE RECOGNITION AS PATTERN RECOGNITION ...........33 2.1 Introduction................................................................................................33 2.2 A General Probabilistic Framework ..........................................................36 2.2.1 Subtasks of Face Recognition...........................................................36 2.2.1.1 Face Classification Tasks......................................................36 2.2.1.2 Face Verification Tasks ........................................................37 2.2.2 Functional Modules ..........................................................................38 2.2.3 Feature Extraction.............................................................................38 2.2.4 Pattern Recognition...........................................................................39 2.3 Groundwork and Literature Review ..........................................................40 2.3.1 Introduction.......................................................................................40 2.3.2 Segmentation.....................................................................................40 2.3.3 Recognition.......................................................................................43 2.3.3.1 Introduction...........................................................................43 2.3.3.2 Classical Approaches............................................................44 2.3.3.3 Feature Based Approaches....................................................45 iii 2.3.3.4 Neural Network Approaches.................................................46 2.3.3.5 Statistical Approaches...........................................................50 2.4 Summary....................................................................................................53 CHAPTER 3. RECENT RESEARCH IN FACE RECOGNITION .....................55 3.1 Introduction................................................................................................55 3.2 AT&T Cambridge Laboratories Face Database ........................................55 3.3 Comparison of Results...............................................................................56 3.4 Discussion..................................................................................................57 3.4.1 Methods.............................................................................................57 3.4.2 Biological Motivation .......................................................................62 3.5 Summary....................................................................................................63 CHAPTER 4. FEATURE SPACES......................................................................64 4.1 Feature Vectors and Related Topics ..........................................................64 4.1.1 Introduction.......................................................................................64 4.1.2 Features.............................................................................................65 4.1.3 Feature Selection and Extraction ......................................................66 4.1.4 Dimension Minimization ..................................................................68 4.1.5 Non-linear Features...........................................................................71 4.1.6 Motivation for DCT-based Features in the NoN Approach..............72 4.2 Network of Networks Hierarchical Clustering ..........................................73 4.3 Summary..........................................................................................................75 CHAPTER 5. TWO-LEVEL DCT COEFFICIENT BASED NoN SYSTEM.....76 5.1 Introduction................................................................................................76 5.2 NoN Face Recognition System Description ..............................................76 5.3 DCT Use in Related Areas.........................................................................78 5.3.1 DCT in Face Recognition .................................................................78 5.3.2 DCT in Text Region Location ..........................................................79 5.4 Motivation for Block-level DCT Coefficients in the NoN System ...........80 5.4.1 Analogy with Biological Processing of Faces by Humans...............80 5.4.2 Existing Efficient Algorithms for the DCT ......................................83 5.5 Level 1: Nested Family of Locally Operating Networks...........................85 5.5.1 Properties of Algorithms...................................................................85 5.5.2 Biologically Motivated Algorithms ..................................................86 5.5.2.1 Direct Accumulation.............................................................87 5.5.2.2 Direct Accumulation with Averaging...................................88 5.5.2.3 Average Absolute Deviation.................................................89 5.6 Level 2: Hierarchically Superior Backpropagation Network ...................90 5.6.1 Backpropagation ...............................................................................90
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