Biometrics, Fall 2024
Details
Course: COMP 388-002 / COMP 488-002 Computer Science Topics
Level: Undergraduate and Graduate
Instructor: Daniel Moreira (dmoreira1@luc.edu)
TA: Fiona Nicdao (fnicdao@luc.edu)
Lectures: MON and WED, 4:15 to 5:30 PM, in person at 408 Mundelein Center
Office Hours: MON and WED evenings, and FRI afternoons,
310 Doyle Center or Zoom, by appointment
Sakai: https://sakai.luc.edu/x/9WVTcd
Overview
How do computers match a person’s fingerprints? Do they still use the same techniques proposed in the XIX century? How do computers identify people captured in a video? Do they leverage the depicted faces only, or can they use other traits such as gait or voice? How about iris recognition as portrayed in the movies? Is it really possible? What happens in the case of people who look very similar, such as identical twins? Which traits are more reliable and robust to impersonation or prone to falsification? These are some of the questions we will address in this course, whose main topic is Biometrics. In a nutshell, Biometrics is the study of techniques to identify individuals through their physical, chemical, and behavioral traits, such as fingerprints, face, iris, DNA, voice, gait, etc. Our focus will be on the technical and ethical aspects of computer-aided Biometrics, discussing the issues of going from simple and benign authentication to the more problematic case of surveillance. The course will have an intense hands-on approach, with the collection of samples and implementation of fingerprint, face, and iris recognition.
Requirements to attend this course are basic programming skills (especially Python). This course and its materials are also available in Sakai.
Schedule (Tentative)
- 08/26 - Syllabus, Course details.
- 08/28 - Basics I, Biometrics, traits, and systems.
- 09/02 - Labor Day, no classes.
- 09/04 - Basics II, Biometric systems, errors, and metrics.
- 09/09 - 1st Coding Class, Metrics’ implementation.
- 09/11 - Fingerprint Recog. I, History and features.
- 09/16 - Fingerprint Recog. II, Acquisition and enhancement.
- 09/18 - Fingerprint Recog. III, Minutiae detection.
- 09/23 - Fingerprint Recog. IV, Data collection.
- 09/25 - 2nd Coding Class, Fingerprint recognition.
- 09/30 - Face Recog. I, Why faces and faces vs. other traits.
- 10/02 - Midterm Preparation, Recap and project discussion.
- 10/07 - Fall Break, no classes.
- 10/09 - Midterm Exam.
- 10/14 - Face Recog. II, Acquisition and enhancement.
- 10/16 - Face Recog. III, Description and matching.
- 10/21 - Face Recog. IV, Deep learning face recognition.
- 10/23 - 3rd Coding Class, Face recognition.
- 10/28 - Iris Recog. I, Why irises and irises vs. other traits.
- 10/30 - Iris Recog. II, Acquisition and enhancement.
- 11/04 - Iris Recog. III, Description and matching.
- 11/06 - 4th Coding Class, Iris recognition.
- 11/11 - Other Traits, Alternative traits and Soft Biometrics.
- 11/13 - Multibiometrics, Data fusion.
- 11/18 - Feature Indexing, Index building and feature querying.
- 11/20 - 1st Invited Talk.
- 11/25 - Office Hours to Conclude Projects, no classes.
- 11/27 - Thanksgiving, no classes.
- 12/02 - Project presentations.
- 12/04 - 2nd Invited Talk.
- 12/09 - Final Exam.
Important Dates
08/26 - First Class.09/02 - Labor Day, no classes.09/09 - 1st Coding Class.09/25 - 2nd Coding Class and 1st Assignment deadline.10/07 - Fall Break, no classes.10/09 - Midterm Exam.- 10/23 - 3rd Coding Class.
- 10/24 - 2nd Assignment deadline.
- 11/06 - 4th Coding Class.
- 11/27 - Thanksgiving, no classes.
- 12/02 - Project presentations.
- 12/09 - Final Exam.
Notebooks (for coding classes)
- Notebook 01, Metrics’ implementation.
- Notebook 02, Fingerprint recognition.
Invited Talks
Information about the talks will be added here as soon as they are settled.
Grading
| Concept | Interval (%) | Concept | Interval (%) | Concept | Interval (%) | Concept | Interval (%) |
|---|---|---|---|---|---|---|---|
| A | [96, 100) | B+ | [88, 92) | C+ | [76, 80) | D+ | [64, 68) |
| A- | [92, 96) | B | [84, 88) | C | [72, 76) | D | [60, 64) |
| B- | [80, 84) | C- | [68, 72) | F | (0, 60) |
Distribution
| Undergraduate (COMP 388-002) | Graduate (COMP 488-002) | |
|---|---|---|
| Assignments (4) | 40% | 25% |
| Exams (2) | 50% | 40% |
| Project | +10% (optional and extra) | 25% |
| Participation | 10% | 10% |
| On the News | +1% (extra) | +1% (extra) |
Assignments
- Assignment 1, due on Sep 25.
- Assignment 2, data, due on Oct 24.
Late Policy
Deduction of 10% of the maximum possible grade for each day of delay.
Exams
- Midterm Exam, 10/09.
- Final Exam, 12/09.
Project
- Written report and presentation, work alone or in pairs.
Possible Topics
- Implementation of complete class attendance system.
- Presentation attack (performance, detection, and mitigation) of fingerprint recognition.
- Presentation attack of face recognition.
- Presentation attack of iris recognition.
- Implementation of recognition of traits other than fingerprints, face, and iris.
- Presentation and implementation of state-of-the-art scientific publications.
- Discussion about the ethical aspects of Biometrics and surveillance.
Participation
- Class Attendance: every presence counts.
- Today-I-missed Statements: every submission counts.
- Grace Cards: use them to pardon class absence or late work.
- Religious holidays will be honored according to the student’s faith, as stated in https://tinyurl.com/4uujcuw2.
Today-I-missed Statements
After every attended class, each student will have to submit (through Sakai) a short paragraph answering one of the following:
- What is your biggest question after class? OR
- What was the most interesting point you learned today?
Inspired by Dr. Sandra Avila.
Grace Cards
Each student has three Grace Cards, which will allow them to avoid losing points because of class absence. They might also use their cards to excuse late-delivered assignments and give a one-week extension. The cards are not valid to dismiss or postpone exam and final project dates. Students may use their cards at their own discretion, as long as they clearly communicate the instructor.
Life happens, be wise.
Biometrics on the News
Posted by the students and the instructor on Sakai.
- Ethical Problems continue to plague biometric studies of Chinese minority groups.
- Illegible Fingerprints.
- Koalas have fingerprints almost identical to ours.
- Major breach found in biometrics system.
Useful Links
Links will be added here as we move forward with the classes.
References
- Jain, Ross, and Nandakumar. Introduction to Biometrics. Springer Books, 2011.
- Jain, Flynn, and Ross. Handbook of Biometrics. Springer Books, 2008.
Learning Outcomes
At the end of the course, students will master the theoretical foundations, key techniques, and applications of Biometrics to real-world scenarios. Their repertoire will include:
- Understanding the fundamentals of Biometrics.
- Developing and implementing Biometric algorithms.
- Assessing the performance of Biometric systems.
- Applying Biometrics to real-world applications.
- Identifying the privacy and ethics issues of Biometric systems.
- Being up-to-date with emerging trends in Biometrics.
Graduate students (within COMP 488-002), in particular, will acquire the following extra skills:
- Reading, peer-reviewing, and writing scientific papers about Biometrics.
- Conducting research in Biometrics, from the design of hypotheses, development of solutions, and comparison to existing baselines, to the design and execution of experiments.
Academic Integrity
Students are expected to adhere to the LUC statements on academic integrity available at https://tinyurl.com/5n6ru62s. These policies fully apply to this course. The penalty for task-wise academic misconduct is losing all the task’s points. Multiple events of misconduct will incur in failing the entire course (with an F grade). All cases of academic misconduct will be reported to the proper department offices. Lastly, students are not allowed to use AI assisted technology (such as ChatGPT) along the entirety of the course, unless explicitly authorized by the instructor.
Accommodations
Students who have disabilities and wish to request academic accommodations are advised to contact the Student Accessibility Center (SAC) at 773-508-3700 or sac@luc.edu as soon as possible. SAC will provide accommodation letters that, once shared with the instructor, will be fully honored as per the terms of their content with no further questions and total confidentiality.
Daniel Moreira
Assistant Professor of Computer Science
Computer scientist with interests in (but not limited to) Computer Vision, Machine Learning, Media Forensics, and Biometrics.