DOCSLIB.ORG

Towards Non-Cooperative Biometric Iris Recognition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Towards Non-Cooperative Biometric Iris Recognition Hugo Pedro Martins Carric¸o Proenc¸a Towards Non-Cooperative Biometric Iris Recognition University of Beira Interior Department of Computer Science October 2006 Hugo Pedro Martins Carric¸o Proenc¸a Towards Non-Cooperative Biometric Iris Recognition Thesis submitted to the Department of Computer Science for the fulfillment of the requirements for the degree of Doctor of Philosophy made under the supervision of Doctor Lu´ıs A. Alexandre, Assistant Professor at the Department of Computer Science of University of Beira Interior, Covilha,˜ Portugal University of Beira Interior Department of Computer Science October 2006 Acknowledgements This is dedicated to everyone that contributed to the achievement of this work. To all of them i let my sincere grateful. First, I would like to express my gratitude to my supervisor, Doctor Lu´ıs A. Alexandre, for his expertise, gentle guidance and encouragement, which ensured that progress was continuously maintained. I am pleased to conclude that our discussions, during the last three years, strongly contributed to the development of this work. I would like to express my grateful to the University of Beira Interior, specially to my colleagues of the Department of Computer Science, for the enjoyable working environment that they contributed to. Also, to the Institute of Telecommunications - Covilha˜ Laboratory - for the given support. I owe particular thanks to the people that contributed for the earliest stage of this work, by offering themselves as volunteers, in the construction of the UBIRIS database, as well to the Optics Center and Center of Multimedia (CREA), both of the University of Beira Interior. Last, but not least, I would like to thank to all the people close to myself in the last years, for their strong support, encouragement, friendship and love. I am grateful for their understanding for the time during which I was absent due to this research work. iii iv Abstract Reliable personal recognition is critical to many processes. Nowadays, modern societies give higher relevance to systems that contribute to the increase of security and reliability, essentially due to terrorism and other extremism or illegal acts. In this context, the use of biometric systems has been increasingly encouraged by public and private entities in order to replace or improve traditional security systems. Basically, the aim is to establish an identity based on who the person is, rather than on what the person possesses or what the person remembers (e.g., an ID card or a password). Within this context, iris is commonly accepted as one of the most accurate biometric traits and has been successfully applied in such distinct domains as airport check-in [107] or refugee control [13]. However, for the sake of accuracy, present iris recognition systems require that subjects stand close (less than two meters) to the imaging camera and look for a period of about three seconds until the data is captured [45]. This cooperative behavior is required in order to capture images with enough quality for the recognition task. However, it strongly restricts the range of domains where iris recognition can be applied, specially those where the subjects’ cooperation is not expectable (e.g., criminal/terrorist seek, missing children). The overcome of this requirement - users’ cooperation - is the main focus of this thesis, i.e., the analysis and proposal of methods for the automatic recognition of individuals, using images of their iris and without requiring them any active participation, in order to achieve accurate covert human recognition. Our main objective is to overcome the users’ cooperation constraints in the biometric iris recognition. Profiting from the extremely low probability of false matches observed in the current iris-based biometric proposals, the external iris visibility and the fact that its capture is minimally intrusive, our aim consists in taking a step ahead in the development of a non-cooperative iris biometric recognition system. v However, it is highly probable that images captured at-a-distance, without users coopera- tion and within highly dynamic capturing environments lead to the appearance of extremely heterogenous images, with several other types of information in the captured iris regions (e.g., iris obstructions by eyelids or eyelashes and reflections). For the terms of our work and of this thesis, all these factors are considered as noise. After analyzing the actual iris recognition methods and finding that they have small robustness to noise factors, our work was oriented to the proposal of more robust iris recognition methods, that must be able to deal with noise and achieve accurate recognition, based in images captured within non-cooperative environments. Essentially, this thesis is related with the purpose of robust noise detection and handling in the iris biometrics, maintaining minimal recognition error rates. Along this thesis several proposals to increase the iris recognition robustness to noise are described, among which we enhance, first, a method to perform the segmentation of noisy iris images, a method to detect and localize the noise regions that result of non-cooperative imaging settings. Second, using the information about the localized noisy iris regions, we propose methods that deal with those noise regions. A method to compute the quality of each extracted feature is described. This method avoids that the features extracted from typically noisy regions can corrupt the biometric signature. Finally, a feature selection and a new iris classification strategy increase the adaptability of the recognition system to the dynamics of non-cooperative environments whereas localized noise regions that could corrupt the whole biometric signature are avoided. Our experiments show a significant increase in the recognition accuracy. Moreover, the fact that our proposals can be used together with most of the iris recognition proposals is regarded as a strong point. vi Resumo O reconhecimento automatico´ da identidade de um indiv´ıduo e´ um processo cr´ıtico para um elevado numero´ de acc¸oes˜ quotidianas. Presentemente, as sociedades atribuem relevanciaˆ crescente a sistemas que contribuam para aumentar os n´ıveis de seguranc¸a e fiabilidade, essencialmente devido a preocupac¸oes˜ com o terrorismo ou outros actos extremistas. Neste contexto, o uso de sistemas biometricos´ tem sido crescentemente encorajado, quer por entidades publicas´ ou privadas, com vista a substituir ou aumentar os n´ıveis de seguranc¸a tradicionais. Basicamente, o objectivo e´ estabelecer uma identidade para um individuo, baseado no que ele e´ em vez de o que ele possui ou o que ele sabe (por exemplo, um cartao˜ de identificac¸ao˜ ou uma palavra-passe). Neste sentido, a ´ıris e´ comummente aceite como um dos sinais biometricos´ mais exactos e tem sido utilizada com sucesso em dom´ınios tao˜ distintos como o controlo de entradas em aeroportos [107] ou o registo de refugiados e eleic¸oes˜ [13]. No entanto, de forma a atingir os n´ıveis de exactidao˜ pretendidos, os sistemas actuais de reconhecimento de ´ıris exigem que os indiv´ıduos a reconhecer se posicionem perto do dispositivo de captura de imagem (menos de dois metros) e olhem para ele por um per´ıodo de cerca de tresˆ segundos, ate´ que os dados necessarios´ sejam registados [45]. Este comportamento cooperativo e´ necessario´ por forma a adquirir imagens com suficiente qualidade, mas restringe a gama de dom´ınios onde a utilizac¸ao˜ de sistemas biometricos´ baseados em ´ıris pode ser efectuada (por exemplo, procura de terroristas ou crianc¸as desaparecidas). Tornar desnecessario´ o procedimento cooperativo por parte dos indiv´ıduos a reconhecer e´ o assunto principal desta tese. Nela sao˜ descritos e experimentalmente comparados metodos´ de efectuar o reconhecimento automatico´ de indiv´ıduos, utilizando imagens da sua ´ıris e sem lhes requerer qualquer acto de participac¸ao˜ no processo, por forma efectuar o recon- hecimento de forma encoberta, sem que os utilizadores sequer se apercebam. Apos´ a analise´ dos metodos´ actuais de reconhecimento biometrico´ baseado em imagens da ´ıris e tendo observado a sua pouca toleranciaˆ a factores de ru´ıdo, o nosso trabalho foi vii orientado para a proposta de metodos´ de reconhecimento mais robustos, capazes de lidar com imagens ruidosas e alcanc¸ar reconhecimento fiavel´ nessa circunstancias. Essencial- mente, esta tese aborda a detecc¸ao˜ e tratamento robusto de ru´ıdo em imagens de ´ıris para propositos´ biometricos,´ mantendo taxas de erro reduzidas. Nesta tese estao˜ descritos metodos´ para aumentar a robustez ao ru´ıdo dos algoritmos de reconhecimento de ´ıris, entre os quais realc¸amos primeiro os metodos´ para efectuar a segmentac¸ao˜ de imagens de ´ıris ruidosas e o metodo´ para detectar e localizar regioes˜ ruidosas em imagens de ´ıris segmentadas e normalizadas. De seguida, usando a informac¸ao˜ acerca da localizac¸ao˜ de cada regiao˜ com ru´ıdo, sao˜ descritos metodos´ que lidam com esse tipo de informac¸ao.˜ E´ descrito um metodo´ para calcular a qualidade de cada uma das caracter´ısticas extra´ıdas para a assinatura biometrica´ e evitar que as caracter´ısticas mais ruidosas possam corromper a assinatura biometrica.´ Descrevemos ainda metodos´ de selecc¸ao˜ de caracter´ısticas e uma nova estrategia´ de classificac¸ao˜ de ´ıris que aumentam a adaptabilidade dos sistemas de reconhecimento aos ambientes onde estao˜ a funcionar, evi- tando simultaneamente que as regioes˜ ruidosas possam corromper a totalidade da assinatura biometrica.´ As experienciaˆ feitas mostram um decrescimo´ substancial nas taxas de erro. O facto das nossas propostas poderem ser utilizadas conjuntamente com a maioria dos algoritmos actuais de reconhecimento de ´ıris e´ visto como uma vantagem. viii Contents List of Figures xv List of Tables xix 1 Introduction 1 1.1 Motivation and Objectives . 3 1.2 Contributions . 5 1.3 Thesis Outline . 7 2 State-Of-The-Art 9 2.1 Biometrics . 9 2.1.1 Modes of Functioning . 12 2.1.2 A Classification of Biometric Systems . 13 2.1.3 Biometric Traits .
Load more

Recommended publications
  • Iris Recognition for Continuous Biometric User Authentication
    Iris Recognition for Continuous Biometric User Authentication Author: Justin Weaver Date: May 30th, 2011 Mentor: Kenrick Mock UAA – Computer Science Department Iris Recognition for Continuous Biometric User Authentication -J. Weaver Page 1 of 13 Table of Contents Abstract.................................................................2 4.1.2 Step Two: Iris Image Normalization..7 1. Introduction......................................................2 4.1.3 Step Three: Hash Generation.............8 2. Overview..........................................................3 4.1.4 Step Four: Hash Matching.................8 2.1 Remote Eye Trackers for Iris Recognition.3 5. Results..............................................................8 3. Requirements....................................................4 5.1 Deviations from Planned Software 3.1 Base Requirements.....................................4 Behavior...........................................................8 3.2 Software Requirements .............................5 5.2 Inherited Code, Hash Generation, and 3.2.1 Software Behavior Specifications......5 Angle Invariance..............................................9 3.3 Other Requirements...................................5 5.3 Revising an Idea from the Proposal.........10 3.3.1 Demonstration Requirements.............5 5.4 Future Work.............................................10 4. Methodology.....................................................6 6. Summary.........................................................11 4.1 Iris Matching
    [Show full text]
  • Iris Recognition, Forensics, and the Future of Privacy Note
    University of Connecticut OpenCommons@UConn Connecticut Law Review School of Law 2017 A Closer Look: Iris Recognition, Forensics, and the Future of Privacy Note Chantelle D. Ankerman Follow this and additional works at: https://opencommons.uconn.edu/law_review Recommended Citation Ankerman, Chantelle D., "A Closer Look: Iris Recognition, Forensics, and the Future of Privacy Note" (2017). Connecticut Law Review. 372. https://opencommons.uconn.edu/law_review/372 DATE DOWNLOADED: Wed May 27 16:55:36 2020 SOURCE: Content Downloaded from HeinOnline Citations: Bluebook 20th ed. Chantelle D. Ankerman, A Closer Look: Iris Recognition, Forensics, and the Future of Privacy, 49 Conn. L. Rev. 1357 (2017). ALWD 6th ed. Chantelle D. Ankerman, A Closer Look: Iris Recognition, Forensics, and the Future of Privacy, 49 Conn. L. Rev. 1357 (2017). APA 7th ed. Ankerman, C. D. (2017). closer look: Iris recognition, forensics, and the future of privacy. Connecticut Law Review, 49(4), 1357-1392. Chicago 7th ed. Chantelle D. Ankerman, "A Closer Look: Iris Recognition, Forensics, and the Future of Privacy," Connecticut Law Review 49, no. 4 (May 2017): 1357-1392 McGill Guide 9th ed. Chantelle D Ankerman, "A Closer Look: Iris Recognition, Forensics, and the Future of Privacy" (2017) 49:4 Conn L Rev 1357. MLA 8th ed. Ankerman, Chantelle D. "A Closer Look: Iris Recognition, Forensics, and the Future of Privacy." Connecticut Law Review, vol. 49, no. 4, May 2017, p. 1357-1392. HeinOnline. OSCOLA 4th ed. Chantelle D Ankerman, 'A Closer Look: Iris Recognition, Forensics, and the Future of Privacy' (2017) 49 Conn L Rev 1357 -- Your use of this HeinOnline PDF indicates your acceptance of HeinOnline's Terms and Conditions of the license agreement available at https://heinonline.org/HOL/License -- The search text of this PDF is generated from uncorrected OCR text.
    [Show full text]
  • Iridian Technologies Biometric Comparison
    Biometric Comparison Guide ZEROTOLERANCE Biometric Comparison Guide Table of Contents (click on the titles below to jump directly to that page) Iris Recognition vs. Facial Recognition Iris Recognition vs. Fingerprint Iris Recognition vs. Hand Geometry Iridian Technologies, Inc. | www.iridiantech.com/atwork | (856) 222-9090 Biometric Comparison Guide Iris Recognition vs. Facial Recognition Overview Facial recognition technology has gained publicity post 9-11 for its ability to “scan” large crowds and populations (random travelers in airports, those attending public events such as the Super Bowl, etc.) As such, it can be useful as a non-invasive attempt to “pick out” those who might fit a similar description within a database, making it a good “surveillance” technology. However, facial recognition is relatively easy to fool. Age, facial hair, surgery, head coverings, and masks all affect results. For this reason, it will most likely remain a surveillance tool instead of a baseline identifier, and will not be used for critical 1: all match applications such as border control, Simplified Passenger Travel (SPT) or restricted access. Return to Table of Contents Iridian Technologies, Inc. | www.iridiantech.com/atwork | (856) 222-9090 Biometric Comparison Guide Iris Recognition vs. Facial Recognition continued Strengths of Facial Recognition • Effective for surveillance applications: • Provides a first level “scan” within an extremely large, low-security situation. • Easy to deploy, can use standard CCTV hardware integrated with face recognition software. • Passive technology, does not require user cooperation and works from a distance. • May be able to use high quality images in an existing database. Weaknesses of Facial Recognition • Lighting, age, glasses, and head/face coverings all impact false reject rates.
    [Show full text]
  • Samsung Galaxy GS9|GS9+ G960U|G965U User Manual
    User manual Table of contents Features 1 Meet Bixby 1 Camera 1 Mobile continuity 1 Dark mode 1 Security 1 Expandable storage 1 Getting started 2 Galaxy S9 3 Galaxy S9+ 4 Assemble your device 5 Charge the battery 6 Start using your device 6 Use the Setup Wizard 6 Transfer data from an old device 7 Lock or unlock your device 8 Accounts 9 Set up voicemail 10 i UNL_G960U_G965U_EN_UM_TN_TA5_021820_FINAL Table of contents Navigation 11 Navigation bar 16 Customize your home screen 18 Bixby 26 Digital wellbeing and parental controls 27 Always On Display 28 Flexible security 29 Mobile continuity 33 Multi window 36 Edge screen 37 Enter text 44 Emergency mode 47 Apps 49 Using apps 50 Uninstall or disable apps 50 Search for apps 50 Sort apps 50 Create and use folders 51 Game Booster 51 ii Table of contents App settings 52 Samsung apps 54 Galaxy Essentials 54 Galaxy Store 54 Galaxy Wearable 54 Game Launcher 54 Samsung Health 55 Samsung Members 56 Samsung Notes 57 Samsung Pay 59 Smart Switch 60 SmartThings 61 Calculator 62 Calendar 63 Camera 65 Clock 71 Contacts 76 Email 81 Gallery 84 iii Table of contents Internet 90 Messages 93 My Files 95 Phone 97 Google apps 105 Chrome 105 Drive 105 Duo 105 Gmail 105 Google 105 Maps 106 Photos 106 Play Movies & TV 106 Play Music 106 Play Store 106 YouTube 106 Additional apps 107 Facebook 107 iv Table of contents Settings 108 Access Settings 109 Search for Settings 109 Connections 109 Wi-Fi 109 Bluetooth 111 Phone visibility 113 NFC and payment 113 Airplane mode 114 Data usage 114 Mobile hotspot 114 Tethering 116
    [Show full text]
  • Implementation of Finger Vein Authentication Using Infra Red Imaging
    International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072 Implementation of Finger Vein Authentication using Infra Red Imaging Ramesh Kumar.C1, Priya.R2, Priyadharshini.S3, Priyavarshini.M4, Varshini Prethi Bobbili5 1Asst Professor, Dept of Electronics and Communication Engineering, Panimalar Engineering College, Chennai 600123,Tamil Nadu. 2,3,4,5 Students, Dept of Electronics and Communication Engineering, Panimalar Engineering College, Chennai 600123, Tamil Nadu. ------------------------------------------------------------------------***------------------------------------------------------------------------- Abstract— As the automation in biometric authentication In this method, the pattern is compared with registered is increasing there is demand for the high security for patterns in a database[13]. This method involves the protecting private information and access control with following four steps : increased speed and accuracy. However, the existing biometric systems are not suitable. Thus a biometric image acquisition authentication based on finger-vein recognition system is pre-processing, proposed. vein extraction Vein patterns are different for each finger and for each person, and as they are hidden underneath the skin’s surface, vein matching forgery is extremely difficult. The proposed system is implemented using maximum curvature points algorithm A finger image captured using infrared light contains veins and various other parameters .The analysis shows that the that have various widths and brightness , which may finger vein recognition is easier and reliable where change with time because of fluctuations in the amount of Simulation is performed using MATLAB. blood in the vein, caused by changes in temperature, physical conditions, etc[1]. To identify a person with high accuracy, the pattern of the thin/thick and clear/unclear Keywords—finger-vein recognition, infra-red imaging veins in an image must be extracted equally[3].
    [Show full text]
  • Iris and Finger Vein Multi Model Recognition System Based on Sift Features
    IJICIS, Vol.15, No. 1 JANURY 2015 International Journal of Intelligent Computing and Information Science IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES F. E. Mohammed E. M. ALdaidamony A. M. Raid Information Science Department , Faculty of Computers and Information System, Mansoura University - Egypt [email protected] [email protected] [email protected] Abstract :Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new method named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of accurate extraction of key-points and clear the noise points without feature loss. Experimental outcomes demonstrate that the normalization and improvement steps are critical for SIFT-based iris recognition and SIFT-based finger vein recognition , the recommended method can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein RecognitionandBiometeric Systems.
    [Show full text]
  • Look This Way | Seeing the Future of Iris Recognition
    LOOK THIS WAY Seeing the Future of Iris Recognition Making sure people are who they say they Some organizations have turned to biometric are can be complex, awkward, and expensive. identification, usually fingerprints, instead. That’s true for law enforcement, border Biometric characteristics—the “things you control, healthcare, and campus or building are” such as fingerprints, voice, and iris security. In fact, it’s true for any organization patterns—are far more reliable than that must reliably authenticate people. PINs, passwords and tokens (“things you know” access cards, and guards can create long and “things you have”). And the most reliable delays and are vulnerable to fraud, theft, biometric is iris recognition. Impossible and human error. Maintaining, enforcing, and to change and almost as difficult to duplicate, growing these systems require significant human iris patterns are certain and enduring budget and resources. —more than fingerprints, voice, face, or gait. Yet, this is how business and government are Adoption of iris recognition is only beginning authenticating people and protecting secure to gain momentum. Although universally facilities today: at travel hubs, at the gate recognized as the most accurate biometric, to the company parking lot, at the turnstile iris recognition technology has been neither to a football game… everywhere. Even though the easiest for the organization to implement the risk of theft and error is high, cards and nor the most convenient for the user. codes are simple for companies to manage Until now. and convenient for people to use. The Cost of Codes and Cards Tokens, pass cards and PINs—what people have and what people know—are easily lost, forgotten, stolen, or shared.
    [Show full text]
  • Multispectral Iris Recognition Analysis: Techniques and Evaluation
    Graduate Theses, Dissertations, and Problem Reports 2006 Multispectral iris recognition analysis: Techniques and evaluation Christopher K. Boyce West Virginia University Follow this and additional works at: https://researchrepository.wvu.edu/etd Recommended Citation Boyce, Christopher K., "Multispectral iris recognition analysis: Techniques and evaluation" (2006). Graduate Theses, Dissertations, and Problem Reports. 2476. https://researchrepository.wvu.edu/etd/2476 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. Multispectral Iris Recognition Analysis: Techniques and Evaluation by Christopher K. Boyce Thesis submitted to the College of Engineering and Mineral Resources at West Virginia University in partial ful¯llment of the requirements for the degree of Master of Science in Electrical Engineering Lawrence Hornak, Ph.D., Chair Arun A. Ross, Ph.D., Co-chair Xin Li, Ph.D. Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2006 Keywords: Iris, Iris Recognition, Multispectral, Iris Segmentation, Spoo¯ng, Iris Anatomy, Iris Feature Extraction Copyright 2006 Christopher K.
    [Show full text]
  • IRIS Recognition by Daugman‟S Method
    Volume IV, Issue VII, July 2015 IJLTEMAS ISSN 2278 - 2540 IRIS Recognition by Daugman‟s Method Miss. A. J. Dixit1, Mr. K. S. Kazi2 Department of Electronics & Telecommunication Engineering 1,2 BMIT, Solapur, (M.S.) India1,2 Abstract- In today’s advanced era it is needed to design the part) and sclera (the white part)as shown in fig. The iris and system which will give highly accurate results regarding pupil considered to be non concentric. The radius of the inner biometric human identification. Iris recognition is considered to border of the iris i.e. it‟s border with the pupil is also not be the best biometric human identification system. Daugman’s constant since the size of pupil increases and decreases method for iris recognition is considered as the efficient & depending upon the amount of light incident to the pupil. accurate iris recognition system as per previous research. This technique is discussed in detail in this paper. Every individual in the world has a unique pattern of iris. This pattern can be extracted from the image of the eye and it is Keywords— Daugman’s Algorithm, Daugman’s Rubber Sheet encoded. This code can be compared to the codes obtained Model, Hamming Distance, Iris Recognition segmentation, from the images of 14 other eyes or the same eye. The results normalization. of this comparison can represent the amount of difference between the compared codes. In this way it can be concluded I. INTRODUCTION if the compared eye patterns belong to the same or different n the case of iris recognition as the iris is located at a remote eye.
    [Show full text]
  • Smart Banking Machine Embedded with Iris Biometric Controlled System
    International Journal of Research in Engineering, Science and Management 234 Volume-2, Issue-4, April-2019 www.ijresm.com | ISSN (Online): 2581-5792 Smart Banking Machine Embedded with Iris Biometric Controlled System K. Jeevitha1, R. Uma2, R. Sangeetha3, A. Preethi4 1Assistant Professor, Department of ECE, R. M. K. Engineering College, Kavaraipettai, India 2,3,4UG Scholar, Department of ECE, R. M. K. Engineering College, Kavaraipettai, India Abstract: Nowadays all ATM card has PIN numbers, so the details using Red-tacton, Iris and security question and its robbers easily hack the ATM card details. This prompted us to answer details while opening the accounts, then customer only increase the security by including the biometric and security access ATM machine. question to the existing system. In this, ATM cards are replaced by Biometrics and Red-tacton module. Moreover, the feature of security question increases privacy to the users in the same way. 2. Relevant work He/she can be free from recalling the PIN numbers. During the Existing banking system gives unique four-digit number to enrollment stage, we store the iris image of the user and the details all account holder. Whenever they need to access the ATM they of the user for the Red-tacton device. If the Iris Image is matched properly, then the next stage of using Red-tacton begins. The enter the four-digit number. It is the simple method transaction secret code stored in the Red-tacton will be passed to the receiver and statistical, so that hackers can easily extract the numbers in the ATM machine, then transaction starts.
    [Show full text]
  • Face–Iris Multimodal Biometric Identification System
    electronics Article Face–Iris Multimodal Biometric Identification System Basma Ammour 1 , Larbi Boubchir 2,* , Toufik Bouden 3 and Messaoud Ramdani 4 1 NDT Laboratory, Electronics Department, Jijel University, Jijel 18000, Algeria; [email protected] 2 LIASD Laboratory, Department of Computer Science, University of Paris 8, 93526 Saint-Denis, France 3 NDT Laboratory, Automatics Department, Jijel University, Jijel 18000, Algeria; bouden_toufi[email protected] 4 LASA Laboratory, Badji Mokhtar-Annaba University, Annaba 23000, Algeria; [email protected] * Correspondence: [email protected] Received: 28 October 2019; Accepted: 17 December 2019; Published: 1 January 2020 Abstract: Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level.
    [Show full text]
  • A New Approach for Human Identification Using the Eye
    i A NEW APPROACH FOR HUMAN IDENTIFICATION USING THE EYE A Thesis Submitted to the Faculty of Purdue University by N. Luke Thomas In Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical and Computer Engineering May 2010 Purdue University Indianapolis, Indiana ii ACKNOWLEDGMENTS First and foremost, I would like to gratefully acknowledge my thesis advisor, Dr. Yingzi (Eliza) Du, for her guidance, supervision, and knowledge during my course of study, research, and thesis work. I would like to express my gratitude to my thesis committee members, Dr. Maher Rizkalla and Dr. Brian King, for their time and helpful comments. I thank Ms. Valerie Lim Diemer, Ms. Jane Simpson, and Ms. Sherrie Tucker for their constructive suggestions and help formatting this thesis. I also would like to sincerely thank my lab mates Mr. Craig Belcher, Mr. Zhi Zhou, Mr. Tron Artavatkun, and Mr. Kai Yang for the technical discussions, their help and support in collecting data for this thesis research, and friendship. I would like to express my appreciation to those subjects who contributed their biometric information for use in the IUPUI multi-wavelength database. I would also like to acknowledge the Department of Computer Science at the University of Beira Interior for providing the UBIRIS database. This work was partially funded by the ONR Young Investigator Award #: N00014-07-1-0788. I would like to thank the ONR for their generous financial support. Finally, and most importantly, I would like to thank my wife for her support, dedication, and love. She has graciously accepted the difficulties and problems that have accompanied my graduate studies.
    [Show full text]