EECS 395, 495: Biometrics

Quarter Offered

Fall : Tu 6-9 ; X. Chen


Working knowledge of Matlab or C/C++ or willingness + time to pick it up quickly


In a security conscious society, biometrics-based authentication and identification have become a central focus for many important applications as biometrics can provide accurate and reliable identification. Biometrics research and technology continue to mature rapidly, driven by pressing industrial and government needs and supported by industrial and government funding. This course offers an introduction to major biometric techniques, the underlying pattern recognition and computer vision basis for these biometrics, scientific testing and evaluation methodologies of biometrics systems, a deeper study of facial recognition, and an examination of the current privacy and social/ethical issues surrounding the technology. The course includes readings from the literature, short writing assignments, and practical experience with current biometric technology.

Course-level Outcomes: As a result of completing this course, the student should:
(1) Have mastered the fundamental concepts and terminology related to biometric recognition of identity,
(2) Understand the flow of processing in three major biometrics modalities,
(3) Be familiar with a selection of current research issues in biometrics,
(4) Be aware of the social impact of biometric technology, and
(5) Understand the underlying technologies including computer vision, pattern recognition and 2D/3D image processing


Instructor's Background: Dr. Chen’s Ph.D. research focuses on face recognition and modeling. His pioneer infrared face recognition research made strides in overcoming the illumination challenge and in establishing the infrared modality as a viable alternative for face recognition. His comparative work involving the 2D intensity, infrared, and 3D modalities inspired him to create a stereo-based approach to face recognition that delivers superior 3D performance, and also proves inexpensive, flexible, and minimally intrusive, unlike 3D commercial scanners. This approach ultimately outperforms its closest commercial 3D counterpart in face recognition experiments. Dr. Chen is currently a Senior Research Scientist and engineering manager at Nokia/HERE. He is also an adjunct professor at IIT and Northwestern University.


  • Lecture 1: Course Introduction; Biometrics overview –What is biometrics? Biometric traits, design and performance evaluation; Biometric research
  • Lecture 2: Big Three Biometrics: Fingerprint, Face and Iris overview; Three case studies: Brandon Mayfield, Boston Marathon bombing suspect identification, India Aadhaar project
  • Lecture 3: Iris Recognition I – patents; the eye and iris; iris image acquisition, enhancement, processing, feature extraction
  • Lecture 4: Iris Recognition II – iris texture analysis, code matching and evaluation, aging effect
  • Lecture 5: Face Recognition I – image acquisition, modalities, processing, features
  • Lecture 6: Face Recognition II – PCA, LDA, 3D, Infrared, data fusion
  • Lecture 7: Fingerprint Recognition – image acquisition, processing, minutiae, matching, and evaluation
  • Lecture 8: Other emerging biometric modalities and technologies survey: Hand, vein, ear, periocular, voice, gait, keystroke, video, multi-modal
  • Lecture 9: Soft biometrics
  • Lecture 10: Privacy and cancelable biometrics