From border crossings to social media, the ability of machines to determine human identity from images has matured to the point where end-users are now interacting with biometric algorithms on a daily basis. However, large-scale deployments in real-world settings bring with them new challenges to a system’s accuracy, security and privacy - thus biometrics remains a lively area of research. This course will introduce students to the fundamentals of biometric system development and evaluation, with an emphasis on the three primary visual modalities: face, fingerprint, and iris. Building from the seminal recognition algorithms, the course will cover the specialized feature descriptors and classification methods most commonly found in biometric application development, as well as the state-of-the-art deep learning architectures that are changing the way we think about visual recognition. Critical for social acceptance, the protection of biometric data is a growing concern. The final part of this course will describe the unique blend of cryptography and pattern recognition that is being developed to revoke and reissue protected biometric data like passwords or PINs.
By the end of the course you will be able to:
The Basics of Biometrics (4 Lectures). Overview of field and applications. Development of biometric authentication. Basic terms, biometric data, biometric characteristics, biometric features, biometric templates and references. Expected properties of biometric identifiers. Basics in biometric errors estimation. Enrollment, verification and identification.
Face Recognition (12 Lectures). Introduction to the face processing pipeline: acquisition, face detection, alignment, feature extraction, matching. Classic subspace methods. Hand-tuned feature descriptors. Deep learning architectures for face representation learning. Distance, similarity and learning-based matching. Face recognition in video. Describable visual attributes. Face pair matching, verification, and identification. Data sets for evaluation. Face image quality. Considerations for social media, mobile authentication, surveillance and other real-world applications.
Fingerprint Recognition (6 Lectures). Fingerprint capture, sensor types, latent fingerprints. Fingerprint image preprocessing, segmentation, binary and skeletal images. Fingerprint singularities, detection of loops, deltas, whirls and cores, using singularities in fingerprints classification. Galton's details, base and complex minutiae, detection of minutiae. Fingerprint recognition, minutiae- and correlation-based methods. Fingerprints in forensics and biometrics, similarities and differences.
Iris Recognition (6 Lectures). Eye and iris morphogenesis, genetic penetrance. Principles of iris image capture, iris sensors. Iris image preprocessing, segmentation, formatting and filtering. Daugman’s method, iris code, statistical properties of the iris code. Other iris coding methods, wavelet analysis.
Multi-Biometric Fusion (3 Lectures). Levels of fusion: sensor, feature, rank, decision. Score normalization and fusion rules. Quality-based fusion and failure prediction.
Biometric System Security (1 Lecture). Secure transfer of biometric data. Secure storage, use of smart cards, principles of match-off-card and match-on-card techniques. Biometrics in the cloud. Points of attack. Privacy models.
Spoofing (2 Lectures) Static and dynamic liveness features. What we want to detect (subversive actions) vs. what we can detect (suspicious actions). Liveness detection in biometrics. Selected liveness detection techniques, frequency analysis for paper printouts detection, pupil dynamics and blood pulse analyses for detection of sophisticated eye and finger spoofing trials.
Template Protection (5 Lectures) Overview of principles from cryptography that help us secure fuzzy data. Template protection strategies: feature protection, key-binding, key-generating, hybrids. Overview of fuzzy vaults, fuzzy commitment, fuzzy extractors and revocable biotokens. Biocryptographic infrastructures for secure template management.
Invited Talks (2 Lectures) Identical Twins and Face Recognition (Prof. Bowyer) and Biometric Data Sets and Evaluation Research at Notre Dame (Prof. Flynn).
Courses in statistics (estimation and hypotheses testing), image processing (1D and 2D filtering), and basic principles of symmetric and asymmetric cryptography will help in faster adoption of the discussed topics, but are not necessarily required.
There are NO required textbooks for this course!
Does this means there are no written materials relevant to this course? Of course not! It means up-to-date information about biometrics is now available on the web. This material is both cheaper and easier to access than traditional textbooks. The resources page of this website contains links to texts that supplement the content covered in the lectures. Interested students may find these texts to be a good starting point for original research down the road.
Here are the formally graded elements of the course and associated weighting:
|Class Participation||10 %||100|
|Homework Assignments (4)||70 %||175 x 4|
|Final Exam||20 %||200|
Class participation includes much more than just attendance. It includes answering questions in class when called upon, and most importantly asking questions and initiating discussions in class. The final exam will cover material from the entire course, with an emphasis on material not covered by the programming assignments. There will be four homework assignments that will require programming and data analysis, with code and a written report as the deliverables.
|Homework #1 (Face Detection)||Released: Monday, September 14th; Due: Monday, September 28th|
|Homework #2 (Face Recognition)||Released: Friday, October 2nd; Due: Friday, October 16th|
|Homework #3 (Iris Recognition)||Released: Friday, October 30th; Due: Friday, November 13th|
|Homework #4 (Fingerprint Spoof Detection)||Released: Monday, November 23rd; Due: Monday, December 7th|
|Final Exam||Thursday, December 17th, 8-10am|
Late and Makeup Policy. In the case of a serious illness or other excused absence, as defined by university policies, coursework submissions will be accepted late by the same number of days as the excused absence. A make-up exam will only be given under extraordinary circumstances. In such cases, a student must consult with the instructor as soon as possible, preferably before the start of the exam.
Students with Disabilities. Any student who has a documented disability and is registered with Disability Services should speak with the professor as soon as possible regarding accommodations. Students who are not registered should contact the Office of Disability Services.