Biometrics, Spring 2022


Course: CSE 40537 / 60537 Biometrics
Level: Undergraduate and Graduate
Instructor: Daniel Moreira (
Teaching Assistant: Jason You (

Lectures: TUE and THR, 3:30 to 4:45 PM, 356A Fitzpatrick Hall
Office Hours: Daniel - MON to FRI, 5:00 to 6:00 PM, 182 Fitzpatrick Hall, Jason - WED, 1:00 to 2:00 PM, 150M Fitzpatrick Hall

Slack: (now deactivated)

Different attacks made by students.

Course grades are now available.



Important Dates

  • 01/28/2022 - 1st assignment due date.
  • 02/03/2022 - Fingerprint data collection.
  • 02/22/2022 - Iris data collection.
  • 03/01/2022 - Dr. Andrey Kuehlkamp’s talk.
  • 03/03/2022 - Midterm exam.
  • 03/04/2022 - 2nd assignment due date.
  • 03/25/2022 - 3rd assignment due date.
  • 04/12/2022 - Mr. Aidan Boyd’s talk.
  • 04/14/2022 - Fingerprint presentation attack day.
  • 04/18/2022 - 4th assignment due date.
  • 04/19/2022 - Iris and face presentation attack day.
  • 04/21/2022 - Grad students’ final report presentation.
  • 05/05/2022 - Final exam, 10:30 to 12:30 PM, 356A Fitzpatrick Hall.

Invited Talks

Dr. Andrey Kuehlkamp
Dr. Andrey Kuehlkamp
Postdoctoral Research Associate at the Center for Research Computing, University of Notre Dame
Diverse Aspects in Advancing Iris Recognition Systems

Are we ready for widespread, mass-scale adoption of iris recognition systems? Following the miniaturization of fingerprint scanners, these have dominated recognition systems and have even become almost commonplace for unlocking cell phones, but what if in the not-so-far-off future they were replaced with iris scanners, would you be comfortable with it? Since its initial introduction in 1993, automated iris recognition has dramatically grown in popularity and soon could become the dominant method for automated recognition. Take for example the largest recognition system in the world — India’s Aadhaar program — which has collected more than 1.1 billion irises from their citizens to be used as the primary identification for banking, pensions, and welfare programs. Even more recently — in 2017 — Somaliland became the first country in the world to use iris recognition as the means for identification in a public election, which had more than 800,000 registered voters. Although a mature technology in many regards, the drastic increase in iris recognition adoption has revealed many opportunities for improvement. In this talk I present an overview of my research, which focuses on improving iris recognition in three ways: speed, accuracy, and robustness.
Mr. Aidan Boyd
Mr. Aidan Boyd
Ph.D. Candidate at the Department of Computer Science and Engineering, University of Notre Dame
Using human perception to train better CNNs

Traditional deep learning is a data-driven process. Images are shown to a CNN and it is expected to learn rules that enable it to perform a task efficiently on new unseen images after training. The problem with this approach is that the model can only learn from the supplied training data. Potentially, this training data is not representative of the entire domain. Although the model classifies training images near perfectly, this learned rule may actually just be coincidental to this data, rather than applying to all images in that task. Additionally, the decision making of these models can be ambiguous and not explainable, meaning it can be difficult to trust the classifications. Humans, however, possess great ability to generalize what they have seen in the past and apply it to the current task. Humans don’t focus on incidental features in data, instead we tend to look at more obvious occurrences that can be easily explained.

This talk will cover two of my recent works where we investigated whether the incorporation of human perception into the training of deep learning models results in better performance on unseen data. Each of these works approach this in different ways, both showing promising results. In the second work, we also investigate whether the models we have trained in this way are more “human-like” in their decision making. These approaches are applied to the domains of iris presentation attack detection and synthetically generated face detection (see for examples of synthetically generated faces).


Concept Point interval Concept Point interval Concept Point interval Concept Point interval
A [94, 100) B+ [88, 89] C+ [78, 79] D [60, 69]
A- [90, 93] B [84, 87] C [74, 77] F [0, 59]
B- [80, 83] C- [70, 73]


  • Total: 100 points
  • Assignments: 10 points (x4)
  • Presentation attack detection report: 20 points
  • Midterm exam: 20 points
  • Final exam: 20 points
  • Late assignments: -1 point per day

Final project grades.

Biometrics on the News

Posted by the students and instructor on Slack:

Daniel Moreira
Daniel Moreira
Assistant Professor of Computer Science

Computer scientist with interests in (but not limited to) Computer Vision, Machine Learning, Media Forensics, and Biometrics.