Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2019
- DOI:
- arXiv:
- arXiv:1906.11889
- Bibcode:
- 2019arXiv190611889J
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Computation and Language;
- Computer Science - Human-Computer Interaction;
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- In: U. Brefeld et al. (Eds.): Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, LNCS 11907, pp. 299-314, Springer Nature, Switzerland, 2020