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The goal of this Junior Research Group is the development of machine learning methods (mainly deep neural network architectures) for the analysis of eye-tracking data for different application areas such as the diagnosis of developmental language disorders, biometry, driver monitoring and adaptive e-learning.
This project is located at the Department of Computer Science at the University of Potsdam, Germany and is funded by the German Federal Ministry for Education and research. For details see Artificial Intelligence for Eye Tracking Data and our AEye-lab repository on Github.
Project duration: 2020-2024
Principal Investigator
Researchers
Dr. Maja Stegenwallner-Schütz
David R. Reich
Research assistants
Jakob Chwastek
Assunta Süss
Detection of ADHD based on Eye Movements during Natural Viewing, ECML/PKDD 2022
Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, and Lena A. Jäger
DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection using Deep Neural Networks, IEEE Transactions on Biometrics, Behavior, and Identity Science 2021
Silvia Makowski, Paul Prasse, David R. Reich, Daniel Krakowczyk, Lena A. Jäger and Tobias Scheffer
[ http | bib]
On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification, Procedia Computer Science 2020
Paul Prasse, Lena A. Jäger, Silvia Makowski, Moritz Feuerpfeil, and Tobias Scheffer
[ http | bib]
Discriminative Viewer Identification using Generative Models of Eye Gaze, Procedia Computer Science 2020
Silvia Makowski, Lena A. Jäger, Lisa Schwetlick, Hans Trukenbrod, Ralf Engbert, and Tobias Scheffer
[ http | bib]
Biometric Identification and Presentation-Attack Detection using Micro- and Macro-Movements of the Eyes, IJCB 2020
Silvia Makowski, Lena A. Jäger, Paul Prasse, and Tobias Scheffer
[ pdf | http | bib]