Data Science and Management Research Laboratory

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• Evaluation of Demographic Bias in Facial Recognition

  • Led by PI Mustafa Atay, September 2020 - present​

  • Current machine learning algorithms allow for a relatively reliable detection, recognition, and categorization of face images comprised of various age, race, and gender. Algorithms with biased data are bound to produce skewed results. It leads to a significant decrease in the performance of state-of-the-art models when applied to images of gender or ethnicity groups. In this research, we study the demographic bias in facial recognition. We aim to report the machine learning algorithms which are inclined towards demographic bias and the ones which mitigate it as well as biased facial image datasets.

• Evaluation of LBP Algorithm for User Authentication with Face Biometric

  • Led by PI Mustafa Atay, October 2019 - October 2021​

  • Local Binary Pattern (LBP) is a discrete yet powerful texture classification scheme, which works particularly well with image classification for facial recognition. In this project, we examine and test various LBP configurations to determine their image classification accuracy. The most favorable configurations of LBP should be examined as a potential way to augment the current username and password standard by enhancing their security with facial biometrics.

• Evaluating Continuous Authentification in Smartphones

  • Led by Co-PI Debzani Deb, October 2019 - present​

  • Each user’s interaction behavior on touchscreens can be quite unique. In this project, we study to what extent touch features can be used for user disambiguation and authentication. We further investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone.

• Computational Framework and Data Science for Identification

  • Entire Team, October 2019 - present​

  • Identity is an emerging critical research field due to digitization of life and the accompanying threats. Data science helps by opening new opportunities for identifying and verifying persons in both cyberspace and the physical world. The project involves extending the single-sign-on WebID protocol to allow virtually any authentication technique to be incorporated into the protocol. It proposes authentication using periocular biometrics and active authentication using behavioral biometrics, and mitigating presentation attacks.