EEG-Based Person Authentication with Variational Universal Background Model

Huyen Tran; Dat Tran; Wanli Ma; Phuoc Nguyen

doi:10.1007/978-3-030-36938-5_25

EEG-Based Person Authentication with Variational Universal Background Model

Huyen Tran, Dat Tran, Wanli Ma, Phuoc Nguyen

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

Silent speech is a convenient and natural way for person authentication as users can imagine speaking their password instead of typing it. However there are inherent noises and complex variations in EEG signals making it difficult to capture correct information and model uncertainty. We propose an EEG-based person authentication framework based on a variational inference framework to learn a simple latent representation for complex data. A variational universal background model is created by pooling the latent models of all users. A likelihood ratio of user claimed model to the background model is constructed for testing whether the claim is valid. Extensive experiments on three datasets show the advantages of our proposed framework.

Original language	English
Title of host publication	Network and System Security - 13th International Conference, NSS 2019, Proceedings
Editors	Joseph K. Liu, Xinyi Huang
Place of Publication	Netherlands
Publisher	Springer
Pages	418-432
Number of pages	15
ISBN (Electronic)	9783030369385
ISBN (Print)	9783030369378
DOIs	https://doi.org/10.1007/978-3-030-36938-5_25
Publication status	Published - 1 Jan 2019
Event	13th International Conference on Network and System Security, NSS 2019 - Sapporo, Japan Duration: 15 Dec 2019 → 18 Dec 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11928 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Network and System Security, NSS 2019
Country/Territory	Japan
City	Sapporo
Period	15/12/19 → 18/12/19

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10.1007/978-3-030-36938-5_25

Cite this

Tran, H., Tran, D., Ma, W., & Nguyen, P. (2019). EEG-Based Person Authentication with Variational Universal Background Model. In J. K. Liu, & X. Huang (Eds.), Network and System Security - 13th International Conference, NSS 2019, Proceedings (pp. 418-432). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11928 LNCS). Springer. https://doi.org/10.1007/978-3-030-36938-5_25

Tran, Huyen ; Tran, Dat ; Ma, Wanli et al. / EEG-Based Person Authentication with Variational Universal Background Model. Network and System Security - 13th International Conference, NSS 2019, Proceedings. editor / Joseph K. Liu ; Xinyi Huang. Netherlands : Springer, 2019. pp. 418-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Silent speech is a convenient and natural way for person authentication as users can imagine speaking their password instead of typing it. However there are inherent noises and complex variations in EEG signals making it difficult to capture correct information and model uncertainty. We propose an EEG-based person authentication framework based on a variational inference framework to learn a simple latent representation for complex data. A variational universal background model is created by pooling the latent models of all users. A likelihood ratio of user claimed model to the background model is constructed for testing whether the claim is valid. Extensive experiments on three datasets show the advantages of our proposed framework.",

author = "Huyen Tran and Dat Tran and Wanli Ma and Phuoc Nguyen",

year = "2019",

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Tran, H, Tran, D , Ma, W & Nguyen, P 2019, EEG-Based Person Authentication with Variational Universal Background Model. in JK Liu & X Huang (eds), Network and System Security - 13th International Conference, NSS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11928 LNCS, Springer, Netherlands, pp. 418-432, 13th International Conference on Network and System Security, NSS 2019, Sapporo, Japan, 15/12/19. https://doi.org/10.1007/978-3-030-36938-5_25

EEG-Based Person Authentication with Variational Universal Background Model. / Tran, Huyen; Tran, Dat ; Ma, Wanli et al.
Network and System Security - 13th International Conference, NSS 2019, Proceedings. ed. / Joseph K. Liu; Xinyi Huang. Netherlands: Springer, 2019. p. 418-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11928 LNCS).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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AU - Nguyen, Phuoc

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N2 - Silent speech is a convenient and natural way for person authentication as users can imagine speaking their password instead of typing it. However there are inherent noises and complex variations in EEG signals making it difficult to capture correct information and model uncertainty. We propose an EEG-based person authentication framework based on a variational inference framework to learn a simple latent representation for complex data. A variational universal background model is created by pooling the latent models of all users. A likelihood ratio of user claimed model to the background model is constructed for testing whether the claim is valid. Extensive experiments on three datasets show the advantages of our proposed framework.

AB - Silent speech is a convenient and natural way for person authentication as users can imagine speaking their password instead of typing it. However there are inherent noises and complex variations in EEG signals making it difficult to capture correct information and model uncertainty. We propose an EEG-based person authentication framework based on a variational inference framework to learn a simple latent representation for complex data. A variational universal background model is created by pooling the latent models of all users. A likelihood ratio of user claimed model to the background model is constructed for testing whether the claim is valid. Extensive experiments on three datasets show the advantages of our proposed framework.

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Tran H, Tran D , Ma W, Nguyen P. EEG-Based Person Authentication with Variational Universal Background Model. In Liu JK, Huang X, editors, Network and System Security - 13th International Conference, NSS 2019, Proceedings. Netherlands: Springer. 2019. p. 418-432. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-36938-5_25

EEG-Based Person Authentication with Variational Universal Background Model

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