Asee peer logo

Project-based Design of a Biometric Face Recognition System

Download Paper |

Conference

2012 ASEE Annual Conference & Exposition

Location

San Antonio, Texas

Publication Date

June 10, 2012

Start Date

June 10, 2012

End Date

June 13, 2012

ISSN

2153-5965

Conference Session

NSF Grantees' Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

11

Page Numbers

25.1081.1 - 25.1081.11

Permanent URL

https://peer.asee.org/21838

Download Count

26

Request a correction

Paper Authors

biography

Ravi P. Ramachandran Rowan University

visit author page

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 Oct. 1990 to Dec. 1992, he worked at the Speech Research Department at AT&T Bell Laboratories. From Jan. 1993 to Aug. 1997, he was a
Research Assistant Professor at Rutgers University. He was also a Senior Speech Scientist at T-Netix from July 1996 to Aug. 1997. Since Sept. 1997, he has been with the Department of Electrical and Computer Engineering at Rowan University where he has been a professor since Sept. 2006. He has served as a consultant to T-Netix, Avenir Inc., and Motorola. From Sept. 2002 to Sept. 2005, he was an Associate Editor for the IEEE Transactions on Speech and Audio Processing and was on the Speech Technical Committee of the Signal Processing society. Since Sept. 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 Circuits and Systems society. His research interests are in digital signal processing, speech processing, pattern recognition, and filter design.

visit author page

biography

Robi Polikar Rowan University

visit author page

Robi Polikar received his Ph.D. from Iowa State University in Ames, Iowa in 2000 in electrical engineering and biomedical engineering. He is a Professor of electrical and computer engineering at Rowan University, Glassboro, N.J., where he chairs the department and also directs the Signal Processing and Pattern Recognition Laboratory. His recent and current works are funded primarily through National Science Foundation’s CAREER and Energy, Power, and Adaptive Systems programs. His primary research interests encompass various related areas of computational intelligence, neural networks, and learning systems, including ensemble based learning, incremental and nonstationary learning, data and decision fusion, and their real-world applications, in which he has more than 120 peer-reviewed publications. He is also interested in developing educational paradigms that allow undergraduate and entry-level graduate students to participate in rigorous computational intelligence research. Polikar is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems.

visit author page

biography

Kevin D. Dahm Rowan University

visit author page

Kevin Dahm is an Associate Professor of chemical engineering at Rowan University. He received his B.S. from WPI in 1992 and his Ph.D. from MIT in 1998, and joined Rowan in 1999. He has received the Joseph J. Martin Award, the Raymond W. Fahien Award, the PIC-III Award, the Corcoran Award and the Mid-Atlantic Section Outstanding Teaching Award from ASEE.

visit author page

biography

Ying Tang Rowan University

visit author page

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, N.J., in 2001. She is currently an Associate Professor of electrical and computer engineering at Rowan University. Her research interests include virtual reality, artificial intelligence, and modeling and scheduling of computer-integrated systems. Tang has led or participated in several research and education projects funded by National Science Foundation, U.S. Department of Transportation, U.S. Navy, the Charles A. and Anne Morrow Lindbergh Foundation, the Christian R. and Mary F. Lindback Foundation, and industry firms. Her work has resulted in more than 60 journal and conference papers and book chapters. 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, and the collaboration with community colleges to develop interactive games in empowering students with engineering literacy and problem-solving.

visit author page

biography

Sachin Shetty Tennessee State University

visit author page

Sachin Shetty received the B.E. degree in computer engineering from Mumbai University, India, in 1998, M.S. in computer science from University of Toledo and Ph.D. in modeling and simulation from Old Dominion University in 2007. He is currently an Assistant Professor in the Electrical and Computer Engineering Department at Tennessee State University. His area of competency includes theoretical and experimental research in network protocols design, security algorithms, and system implementation of cognitive radio networks and wireless sensor networks. He has authored and coauthored more than 30 technical refereed and non-refereed papers in various conferences, international journal articles, and book chapters in research and pedagogical techniques.

visit author page

biography

Richard J. Kozick Bucknell University

visit author page

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 a faculty member at Bucknell University, where he is currently Professor of electrical engineering. His research interests are in the areas of statistical signal processing, sensor networking, sensor fusion, and communications.

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 has published more than 100 manuscripts in peer-reviewed journals and conferences. He serves on the editorial boards of the Journal of the Franklin Institute and the EURASIP Journal on Wireless Communications and Networking, and he was an Associate Editor for IEEE Signal Processing Letters.

visit author page

biography

Robert M. Nickel Bucknell University

visit author page

R. M. Nickel received the Diplom-Ing. degree in electrical engineering from the Rheinisch-Westfälische Technische Hochschule (RWTH), Aachen, Germany, in 1994 and the M.S. degree in electrical engineering and the Ph.D. degree from the University of Michigan, Ann Arbor, in 1997 and 2001, respectively. During the 2001-02 academic year, he was an adjunct faculty member in the Department of Electrical Engineering and Computer Science, University of Michigan. From 2002 until 2007, he was a faculty
member at the Pennsylvania State University, University Park. Since the fall of 2007, he has been a faculty member at the Electrical Engineering Department, Bucknell University, Lewisburg, Penn. His main research interests include speech signal processing and general signal theory.

visit author page

biography

Steven H. Chin Rowan University

visit author page

Steven Chin is a graduate of Rutgers University, College of Engineering, where he holds a bachelor of science and a doctoral degree in electrical engineering. He received his master of science in electrical engineering from the Johns Hopkins University. His background is in the area of signal/image processing, with application to communication systems. He previously was with the Catholic University of America before joining Rowan University in 1997 as the Associate Dean of engineering. He presently serves as Interim Dean of engineering at Rowan University.

visit author page

Download Paper |

Abstract

PROJECT BASED DESIGN OF A BIOMETRIC FACE RECOGNITION SYSTEMBiometrics is the science of recognizing and authenticating people using their physiologicalfeatures. Interest in biometrics has increased significantly after the 9/11 attacks. Border andimmigration control, restricted access to facilities and information systems, cybersecurity, crimeinvestigations and forensic analysis are just a few of the primary application areas of biometricsused by commercial, government and law enforcement agencies. The biometrics market hasgrown from $2.7 billion in 2007 to an expectation of $7.1 billion by 2012 with a compoundannual growth rate of 21.3 percent. There is much research interest in different biometricsystems, notably, face recognition. Face recognition systems have advantages including ease ofuse and implementation, low cost and high user acceptance. In addition, they can be easilyintegrated (no special hardware except for a built-in Web camera) with many devices includingdesktops, laptops, cell phones, wireless access points, iPhones, iPads and PDAs.This paper is about a face recognition project focused on open-ended design that is part of asenior undergraduate course on biometric systems. In implementing a face recognition system,students go through each step, namely, preprocessing, feature extraction, classification (trainingand use in rendering a decision) and performance evaluation. The AT&T database is used toshow students that robustness to mismatched training and testing conditions is a significantpractical issue. The open-ended aspects include researching different robust features,implementing different classifiers and investigating feature and classifier fusion to augmentperformance. The student learning outcomes of the project include: Enhanced application of math skills Enhanced software implementation skills Enhanced interest in biometrics Enhanced ability to read papers and apply algorithms (like robust feature extraction) to achieve a better design thereby providing research experience. 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.

Ramachandran, R. P., & Polikar, R., & Dahm, K. D., & Tang, Y., & Shetty, S., & Kozick, R. J., & Nickel, R. M., & Chin, S. H. (2012, June), Project-based Design of a Biometric Face Recognition System Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. https://peer.asee.org/21838

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2012 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015