One-Shot-Learning for Visual Lip-Based Biometric Authentication

Carrie Wright*, Darryl Stewart

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Lip-based biometric authentication is the process of verifying an individual’s identity based on visual information taken from lips whilst speaking. To date research in this area has involved more traditional approaches and inconsistent results that are difficult to compare. This work aims to push the field forward through the application of deep learning. A deep artificial neural network using spatiotemporal convolutional and bidirectional gated recurrent unit layers is trained end-to-end. For the first time one-shot-learning is applied to lip-based biometric authentication by implementing a siamese network architecture, meaning the model only needs a single prior example in order to authenticate new users. This approach sets a new state-of-the-art performance for lip-based biometric authentication on the XM2VTS dataset and Lausanne protocol with an equal error rate of 0.93% on the evaluation set and a false acceptance rate of 1.07% at a 1% false rejection rate.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings
EditorsGeorge Bebis, Bahram Parvin, Richard Boyle, Darko Koracin, Daniela Ushizima, Sek Chai, Shinjiro Sueda, Xin Lin, Aidong Lu, Daniel Thalmann, Chaoli Wang, Panpan Xu
PublisherSpringer
Pages405-417
Number of pages13
ISBN (Print)9783030337193
DOIs
Publication statusPublished - 21 Oct 2019
Event14th International Symposium on Visual Computing, ISVC 2019 - Nevada, United States
Duration: 07 Oct 201909 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11844 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Symposium on Visual Computing, ISVC 2019
CountryUnited States
CityNevada
Period07/10/201909/10/2019

Keywords

  • Biometric authentication
  • Lip-based
  • One-shot-learning
  • Siamese network
  • XM2VTS

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wright, C., & Stewart, D. (2019). One-Shot-Learning for Visual Lip-Based Biometric Authentication. In G. Bebis, B. Parvin, R. Boyle, D. Koracin, D. Ushizima, S. Chai, S. Sueda, X. Lin, A. Lu, D. Thalmann, C. Wang, & P. Xu (Eds.), Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings (pp. 405-417). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11844 LNCS). Springer. https://doi.org/10.1007/978-3-030-33720-9_31