Deep Eyedentification: Biometric Identification Using Micro-Movements of the Eye

Lena A. Jäger, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler, Tobias Scheffer

ECML-PKDD 2019 pp. 299-314

doi:10.1007/978-3-030-46147-8_18 /ecmlpkdd/2019/jager2019ecmlpkdd-deep/

Abstract

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.

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Text

Jäger et al. "Deep Eyedentification: Biometric Identification Using Micro-Movements of the Eye." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46147-8_18

Markdown

[Jäger et al. "Deep Eyedentification: Biometric Identification Using Micro-Movements of the Eye." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/jager2019ecmlpkdd-deep/) doi:10.1007/978-3-030-46147-8_18

BibTeX

@inproceedings{jager2019ecmlpkdd-deep,
  title     = {{Deep Eyedentification: Biometric Identification Using Micro-Movements of the Eye}},
  author    = {Jäger, Lena A. and Makowski, Silvia and Prasse, Paul and Liehr, Sascha and Seidler, Maximilian and Scheffer, Tobias},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2019},
  pages     = {299-314},
  doi       = {10.1007/978-3-030-46147-8_18},
  url       = {https://mlanthology.org/ecmlpkdd/2019/jager2019ecmlpkdd-deep/}
}