Instructor: Prof. Harry Wechsler firstname.lastname@example.org
Course Description / Goals and Outcomes /: Learn basic principles and methods for automatic personal authentication and identity management. Technologies include among others face, fingerprint, iris recognition, gait, and voice. Additional topics cover multimodal biometrics, system design, performance evaluation, and security and privacy concerns. Course at the interface between (1) image analysis; (2) data mining, machine learning, pattern recognition; (3) security , integrity, privacy; and (4) distributed systems and interoperability.
Time, Day, and Venue: W – Wednesday, 7:20 pm - 10:00 pm, Robinson Hall B224
First day of classes, Wednesday, August 29
[Labor Day, no class, Monday, September 3]
Thanksgiving recess November 21 – 25 / No class Wednesday, November 21
Last day of classes, Wednesday, December 5
Final Exam: Wednesday, December 12, 7:30 – 10:15 pm
Office Hours: Wednesday, 6:15 pm – 7:15 pm (ENGR - 4448)
Textbook: Jain, Ross, and Nandakumar, Introduction to Biometrics, Springer, 2011
References:  H. Wechsler, Reliable Face Recognition Methods, Springer 2007;  R.M. Bolle, J. H. Connell, S. Pankanti, N.K. Ratha, and A. W. Senior, Guide to Biometrics, Springer 2004.
Motivation and Learning Objectives: Biometrics, the science of recovering or verifying a person's identity, measures the physical and behavioral characteristics that make people unique — including fingerprints, an eye's retina or iris, face, hand geometry, gait, signature, and voice —and uses those measurements for personal authentication. Biometrics is related to forensics, which uses and interprets physical evidence for legal purposes. The importance of biometrics lies in the fact that traditional means of identification and verification are often unreliable or cumbersome. Passwords are difficult to remember and easy to steal. Keys, driver's licenses, and passports can be lost or forged. The human body (and its behavior), on the other hand, can't be forgotten, stolen, or misplaced (but can still be spoofed). Practical applications for biometrics are wide spread and include maintaining the security for both physical and cyber space using continuous identity management. In particular, biometrics aids in controlling access to an office, computer network or an ATM, smart cards, wireless communication; confirming the identity of buyers and sellers to make electronic commerce safe and reliable; confirming student identity for distant learning; and safeguarding electronic records related to health care services.
· biometric and identity management tasks, protocols, standards, and technologies
· primer on image analysis and computer vision
· primer on machine learning and pattern recognition
· face recognition (2D and 3D)
· fingerprint recognition
· iris recognition
· gait recognition
· speaker verification
· performance evaluation and error analysis
· security, privacy, ethics, and interoperability
· Homework – 20%
· Mid Term – Wednesday, October 17 – 20 %
· Term (Team) Project – 30 %
· Final – 30 %
You are expected to abide by the GMU honor code. Homework assignments and exams are individual efforts. Information on the university honor code can be found at http://academicintegrity.gmu.edu/honorcode/.
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies