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

Carrie Wright; Darryl Stewart

doi:10.1007/978-3-030-33720-9_31

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

Carrie Wright^*, Darryl Stewart

^*Corresponding author for this work

Queen's University Belfast

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (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 language	English
Title of host publication	Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings
Editors	George Bebis, Bahram Parvin, Richard Boyle, Darko Koracin, Daniela Ushizima, Sek Chai, Shinjiro Sueda, Xin Lin, Aidong Lu, Daniel Thalmann, Chaoli Wang, Panpan Xu
Publisher	Springer
Pages	405-417
Number of pages	13
ISBN (Print)	9783030337193
DOIs	https://doi.org/10.1007/978-3-030-33720-9_31
Publication status	Published - 21 Oct 2019
Event	14th International Symposium on Visual Computing, ISVC 2019 - Nevada, United States Duration: 07 Oct 2019 → 09 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11844 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Symposium on Visual Computing, ISVC 2019
Country/Territory	United States
City	Nevada
Period	07/10/2019 → 09/10/2019

Keywords

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

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-33720-9_31Licence: Unspecified

Lip-based biometric authentication
Wright, C. (Author), Stewart, D. (Supervisor), Dec 2019
Student thesis: Doctoral Thesis › Doctor of Philosophy

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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

Wright, Carrie ; Stewart, Darryl. / One-Shot-Learning for Visual Lip-Based Biometric Authentication. Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings. editor / George Bebis ; Bahram Parvin ; Richard Boyle ; Darko Koracin ; Daniela Ushizima ; Sek Chai ; Shinjiro Sueda ; Xin Lin ; Aidong Lu ; Daniel Thalmann ; Chaoli Wang ; Panpan Xu. Springer, 2019. pp. 405-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{de635120be8e44cd9bd5b87e3bae8d30,

title = "One-Shot-Learning for Visual Lip-Based Biometric Authentication",

abstract = "Lip-based biometric authentication is the process of verifying an individual{\textquoteright}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.",

keywords = "Biometric authentication, Lip-based, One-shot-learning, Siamese network, XM2VTS",

author = "Carrie Wright and Darryl Stewart",

year = "2019",

month = oct,

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doi = "10.1007/978-3-030-33720-9_31",

language = "English",

isbn = "9783030337193",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "405--417",

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booktitle = "Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings",

note = "14th International Symposium on Visual Computing, ISVC 2019 ; Conference date: 07-10-2019 Through 09-10-2019",

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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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11844 LNCS, Springer, pp. 405-417, 14th International Symposium on Visual Computing, ISVC 2019, Nevada, United States, 07/10/2019. https://doi.org/10.1007/978-3-030-33720-9_31

One-Shot-Learning for Visual Lip-Based Biometric Authentication. / Wright, Carrie; Stewart, Darryl.
Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings. ed. / George Bebis; Bahram Parvin; Richard Boyle; Darko Koracin; Daniela Ushizima; Sek Chai; Shinjiro Sueda; Xin Lin; Aidong Lu; Daniel Thalmann; Chaoli Wang; Panpan Xu. Springer, 2019. p. 405-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11844 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - One-Shot-Learning for Visual Lip-Based Biometric Authentication

AU - Wright, Carrie

AU - Stewart, Darryl

PY - 2019/10/21

Y1 - 2019/10/21

N2 - 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.

AB - 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.

KW - Biometric authentication

KW - Lip-based

KW - One-shot-learning

KW - Siamese network

KW - XM2VTS

U2 - 10.1007/978-3-030-33720-9_31

DO - 10.1007/978-3-030-33720-9_31

M3 - Conference contribution

AN - SCOPUS:85076157090

SN - 9783030337193

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 405

EP - 417

BT - Advances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings

A2 - Bebis, George

A2 - Parvin, Bahram

A2 - Boyle, Richard

A2 - Koracin, Darko

A2 - Ushizima, Daniela

A2 - Chai, Sek

A2 - Sueda, Shinjiro

A2 - Lin, Xin

A2 - Lu, Aidong

A2 - Thalmann, Daniel

A2 - Wang, Chaoli

A2 - Xu, Panpan

PB - Springer

T2 - 14th International Symposium on Visual Computing, ISVC 2019

Y2 - 7 October 2019 through 9 October 2019

ER -

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

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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Student theses

Lip-based biometric authentication

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