We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-tracking signal. Compared to prior work, the network attains a lower error rate by one order of magnitude and is faster by two orders of magnitude: it identifies users accurately within seconds.
@article{arxiv.1906.11889,
title = {Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye},
author = {Lena A. Jäger and Silvia Makowski and Paul Prasse and Sascha Liehr and Maximilian Seidler and Tobias Scheffer},
journal= {arXiv preprint arXiv:1906.11889},
year = {2020}
}