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Design and Performance Evaluation of a Biometric Iris Verification System

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Conference

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

NSF Grantees’ Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

14

Page Numbers

26.458.1 - 26.458.14

DOI

10.18260/p.23796

Permanent URL

https://peer.asee.org/23796

Download Count

200

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Paper Authors

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Ravi P. Ramachandran Rowan University

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Ravi P. Ramachandran received the B. Eng degree (with great distinction) from Concordia University in 1984, the M. Eng degree from McGill University in 1986 and the Ph.D. degree from McGill University in 1990. From October 1990 to December 1992, he worked at the Speech Research Department at AT&T Bell Laboratories. From January 1993 to August 1997, he was a Research Assistant Professor at Rutgers University. He was also a Senior Speech Scientist at T-Netix from July 1996 to August 1997. Since September 1997, he is with the Department of Electrical and Computer Engineering at Rowan University where he has been a Professor since September 2006. He has served as a consultant to T-Netix, Avenir Inc., Motorola and Focalcool. From September 2002 to September 2005, he was an Associate Editor for the IEEE Transactions on Speech and Audio Processing and was on the Speech Technical Committee for the IEEE Signal Processing society. Since September 2000, he has been on the Editorial Board of the IEEE Circuits and Systems Magazine. Since May 2002, he has been on the Digital Signal Processing Technical Committee for the IEEE Circuits and Systems society. His research interests are in digital signal processing, speech processing, biometrics, pattern recognition and filter design.

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Liang Hong Tennessee State University

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Dr. Liang Hong received the B.S. and the M.S. degrees in Electrical Engineering from Southeast University, Nanjing, China in 1994 and 1997, respectively, and the Ph.D. degree in Electrical Engineering from University of Missouri, Columbia, Missouri in 2002. Since August 2003, he has been with the Department of Electrical & Computer Engineering at Tennessee State University where he is now Full Professor. His research interests include cognitive radio, modulation classification, wireless multimedia communications, and engineering education.

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Sachin Shetty Tennessee State University

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Sachin Shetty is currently an Assistant Professor in the Department of Electrical and Computer Engineering at Tennessee State University. He received his Ph.D. degree in Modeling and Simulation from Old Dominion University in 2007 under the supervision of Prof. Min Song. His research interests lie at the intersection of computer networking, network security and machine learning. Recently, he has been working on security issues in cloud computing, cognitive radio networks, and wireless sensor networks. Over the years, he has secured funding over $3 million from NSF, AFOSR, DOE, DHS, TBR and local industry for research and educational innovations. He has authored and coauthored over 30 technical refereed and non-refereed papers in various conferences, international journal articles, book chapters in research and pedagogical techniques. He is the director of the Cyber Defense and Security Visualization Laboratory

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Kevin D. Dahm Rowan University

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Kevin Dahm is a Professor of Chemical Engineering at Rowan University. He earned his BS from Worcester Polytechnic Institute (92) and his PhD from Massachusetts Institute of Technology (98). He has published two books, "Fundamentals of Chemical Engineering Thermodynamics" and "Interpreting Diffuse Reflectance and Transmittance." He has also published papers on effective use of simulation in engineering, teaching design and engineering economics, and assessment of student learning.

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Richard J. Kozick Bucknell University

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Richard J. Kozick received the B.S. degree from Bucknell University in 1986, the M.S. degree from Stanford University in 1988, and the Ph.D. degree from the University of Pennsylvania in 1992, all in electrical engineering. From 1986 to 1989 and from 1992 to 1993 he was a Member of Technical Staff at AT&T Bell Laboratories. Since 1993, he has been with the Electrical and Computer Engineering Department at Bucknell University, where he is currently Professor. His research interests are in the areas of statistical signal processing and communications.

Dr. Kozick received a "2006 Best Paper Award" from the IEEE Signal Processing Society and the Presidential Award for Teaching Excellence from Bucknell University in 1999. He serves on the editorial boards of the Journal of the Franklin Institute and the EURASIP Journal on Wireless Communications and Networking.

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Robert M Nickel Bucknell University

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Robert M. Nickel received a Dipl.-Ing. degree in electrical engineering from the RWTH Aachen University, Germany, in 1994, and a Ph.D. in electrical engineering from the University of Michigan, Ann Arbor, Michigan, in 2001. During the 2001/2002 academic year he was an adjunct faculty in the Department of Electrical Engineering and Computer Science at the University of Michigan. From 2002 until 2007 he was a faculty member at the Pennsylvania State University, University Park, Pennsylvania. Since the fall of 2007 he is a faculty member at the Electrical Engineering Department of Bucknell University, Lewisburg, Pennsylvania. During the 2010/2011 academic year he was a Marie Curie Incoming International Fellow at the Institute of Communication Acoustics, Ruhr-Universität Bochum, Germany. His main research interests include speech signal processing, general signal theory, and time-frequency analysis.

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Robi Polikar Rowan University

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Robi Polikar is a Professor of Electrical and Computer Engineering at Rowan University, in Glassboro, NJ. He has received his B.Sc. degree in electronics and communications engineering from Istanbul Technical University, Istanbul, Turkey in 1993, and his M.Sc and Ph.D. degrees, both co-majors in electrical engineering and biomedical engineering, from Iowa State University, Ames, IA in 1995 and 2000, respectively. His current research interests within computational intelligence include ensemble systems, incremental and nonstationary learning, and various applications of pattern recognition in bioinformatics and biomedical engineering. He is a member of IEEE, ASEE, Tau Beta Pi and Eta Kappa Nu. His recent and current works are funded primarily through NSF’s CAREER and Energy, Power and Adaptive Systems (EPAS) programs.

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Ying Tang Rowan University

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Ying Tang received the B.S. and M.S. degrees from the Northeastern University, P. R. China, in 1996 and 1998, respectively, and Ph.D degree from New Jersey Institute of Technology, Newark, NJ, in 2001. She is currently a Professor of Electrical and Computer Engineering (ECE) at Rowan University, Glassboro, NJ. Her research interests include virtual reality and augmented reality, artificial intelligence, and modeling and scheduling of computer-integrated systems. Dr. Tang is very active in adapting and developing pedagogical methods and materials to enhance engineering education. Her most recent educational research includes the collaboration with Tennessee State University and local high schools to infuse cyber-infrastructure learning experience into the pre-engineering and technology-based classrooms, the collaboration with community colleges to develop interactive games in empowering students with engineering literacy and problem-solving, the integration of system-on-chip concepts across two year Engineering Science and four year ECE curricula, and the implementation of an educational innovation that demonstrates science and engineering principles using an aquarium. Her work has resulted in over 100 journal and conference papers and book chapters.

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Steven H Chin Rowan University

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Steven H. Chin is currently the Associate Dean of Engineering at Rowan University. He has been in this position since 1997, while serving as Interim Dean from 2010-2012. He has a Bachelor of Science in Electrical Engineering and Ph.D. from Rutgers University, and Masters of Science in Electrical Engineering from the Johns Hopkins University. His specialization areas are in signal processing and communication system. His current interests include STEM education, and academic partnerships.

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Abstract

DESIGN AND PERFORMANCE EVALUATION OF A BIOMETRIC IRIS VERIFICATION SYSTEMBiometrics is the science of recognizing and authenticating people using their physiologicalfeatures. Border and immigration control, restricted access to facilities and information systems,cybersecurity, crime investigations and forensic analysis are just a few of the primary applicationareas of biometrics used by commercial, government and law enforcement agencies. The globalbiometrics market has a compound annual growth rate of 21.3 percent. There is much researchinterest in different biometric systems, notably, iris recognition. Iris recognition systems haveadvantages including ease of use and implementation and high performance. The cost isdiminishing and user acceptance is becoming higher.This paper is about an iris verification project focused on design and performance evaluationunder both matched and mismatched training and testing conditions. Training is alwaysperformed on clean iris images. Testing is performed on both clean and noisy iris images. Thisproject is part of a senior undergraduate course on biometric systems. In implementing an irisrecognition system, students go through each step, namely, preprocessing, feature extraction,classification (training and use in rendering a decision) and performance evaluation. The ChineseAcademy of Sciences - Institute of Automation (CASIA) eye image database known as theCASIA-Iris-Interval-v3 database is used to show students that robustness to mismatched trainingand testing conditions is a significant practical issue. The student learning outcomes of theproject include: Enhanced application of math skills Enhanced software implementation skills Enhanced interest in biometrics Enhanced ability to analyze experimental results Enhanced communication skills Comprehension of the importance of vertical integration in that students realize that their experiences are part of a flow that contributes to a unified knowledge base.The assessment results are very encouraging with respect to the achievement of the learningoutcomes. The assessment instruments include: Student surveys (target versus control group comparison that includes a statistical analysis) Faculty tracking of student learning outcomes based on student work Faculty evaluation of student work based on significant rubrics A concept inventory test

Ramachandran, R. P., & Hong, L., & Shetty, S., & Dahm, K. D., & Kozick, R. J., & Nickel, R. M., & Polikar, R., & Tang, Y., & Chin, S. H. (2015, June), Design and Performance Evaluation of a Biometric Iris Verification System Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23796

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