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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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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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Vorheriges Kapitel LYRICS: A General Interface Layer to Integrate Logic Inference and Deep Learning
Nächstes Kapitel Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization
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Metadaten
Titel
Deep Eyedentification: Biometric Identification Using Micro-movements of the Eye
verfasst von
Lena A. Jäger
Silvia Makowski
Paul Prasse
Sascha Liehr
Maximilian Seidler
Tobias Scheffer
Copyright-Jahr
2020
Buch
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-030-46146-1

Electronic ISBN: 978-3-030-46147-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-46147-8_18