Personalization of gaze direction estimation with deep learning

Zoltán Tősér, Róbert A. Rill, Kinga Faragó, László A. Jeni, A. Lőrincz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

There is a growing interest in behavior based biometrics. Although biometric data has considerable variations for an individual and may be faked, yet the combination of such ‘weak experts’ can be rather strong. A remotely detectable component is gaze direction estimation and thus, eye movement patterns. Here, we present a novel personalization method for gaze estimation systems, which does not require a precise calibration setup, can be non-obtrusive, is fast and easy to use. We show that it improves the precision of gaze direction estimation algorithms considerably. The method is convenient; we exploit 3D face model reconstruction for the enrichment of a small number of collected data artificially.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 39th Annual German Conference on AI, KI 2016, Proceedings
PublisherSpringer Verlag
Pages200-207
Number of pages8
Volume9904 LNAI
ISBN (Print)9783319460727
DOIs
Publication statusPublished - 2016
Event39th German Conference on Artificial Intelligence, KI 2016 - Klagenfurt, Austria
Duration: Sep 26 2016Sep 30 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9904 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other39th German Conference on Artificial Intelligence, KI 2016
CountryAustria
CityKlagenfurt
Period9/26/169/30/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tősér, Z., Rill, R. A., Faragó, K., Jeni, L. A., & Lőrincz, A. (2016). Personalization of gaze direction estimation with deep learning. In Advances in Artificial Intelligence - 39th Annual German Conference on AI, KI 2016, Proceedings (Vol. 9904 LNAI, pp. 200-207). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9904 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-46073-4_20