Face Anti-spoofing Based on Client Identity Information and Depth Map

Yu Wang; Mingtao Pei; Zhengang Nie; Xinmu Qi

doi:10.1007/978-3-031-46305-1_31

Face Anti-spoofing Based on Client Identity Information and Depth Map

Yu Wang, Mingtao Pei^*, Zhengang Nie, Xinmu Qi

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Face anti-spoofing (FAS) is an essential prerequisite for face recognition. In most methods, FAS is usually performed before face recognition and the client identity information is not utilized. Since presentation attacks (PAs) are always aimed at a certain client, the client identity information can provide useful clues for FAS task. In this paper, we propose a face anti-spoofing method based on client identity information using Siamese network. We applied FAS after face recognition to utilize the client identity information. As the real face and fake face have different properties, we use different weights for the two subnetworks of the Siamese network to extract features for real face and fake face, respectively. In addition, we employ depth map as auxiliary information to improve the performance. We perform experiments on SiW, CASIA-FASD and Replay-Attack datasets to demonstrate the validity of our method.

Original language	English
Title of host publication	Image and Graphics - 12th International Conference, ICIG 2023, Proceedings
Editors	Huchuan Lu, Risheng Liu, Wanli Ouyang, Hui Huang, Jiwen Lu, Jing Dong, Min Xu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	380-389
Number of pages	10
ISBN (Print)	9783031463044
DOIs	https://doi.org/10.1007/978-3-031-46305-1_31
Publication status	Published - 2023
Event	12th International Conference on Image and Graphics, ICIG 2023 - Nanjing, China Duration: 22 Sept 2023 → 24 Sept 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14355 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Image and Graphics, ICIG 2023
Country/Territory	China
City	Nanjing
Period	22/09/23 → 24/09/23

Keywords

Client identity information
Depth map
Face anti-spoofing
Siamese network

Access to Document

10.1007/978-3-031-46305-1_31

Cite this

Wang, Y., Pei, M., Nie, Z., & Qi, X. (2023). Face Anti-spoofing Based on Client Identity Information and Depth Map. In H. Lu, R. Liu, W. Ouyang, H. Huang, J. Lu, J. Dong, & M. Xu (Eds.), Image and Graphics - 12th International Conference, ICIG 2023, Proceedings (pp. 380-389). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14355 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-46305-1_31

Wang, Yu ; Pei, Mingtao ; Nie, Zhengang et al. / Face Anti-spoofing Based on Client Identity Information and Depth Map. Image and Graphics - 12th International Conference, ICIG 2023, Proceedings. editor / Huchuan Lu ; Risheng Liu ; Wanli Ouyang ; Hui Huang ; Jiwen Lu ; Jing Dong ; Min Xu. Springer Science and Business Media Deutschland GmbH, 2023. pp. 380-389 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{65af5486ffa8445f996e17c700f666a2,

title = "Face Anti-spoofing Based on Client Identity Information and Depth Map",

abstract = "Face anti-spoofing (FAS) is an essential prerequisite for face recognition. In most methods, FAS is usually performed before face recognition and the client identity information is not utilized. Since presentation attacks (PAs) are always aimed at a certain client, the client identity information can provide useful clues for FAS task. In this paper, we propose a face anti-spoofing method based on client identity information using Siamese network. We applied FAS after face recognition to utilize the client identity information. As the real face and fake face have different properties, we use different weights for the two subnetworks of the Siamese network to extract features for real face and fake face, respectively. In addition, we employ depth map as auxiliary information to improve the performance. We perform experiments on SiW, CASIA-FASD and Replay-Attack datasets to demonstrate the validity of our method.",

keywords = "Client identity information, Depth map, Face anti-spoofing, Siamese network",

author = "Yu Wang and Mingtao Pei and Zhengang Nie and Xinmu Qi",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 12th International Conference on Image and Graphics, ICIG 2023 ; Conference date: 22-09-2023 Through 24-09-2023",

year = "2023",

doi = "10.1007/978-3-031-46305-1_31",

language = "English",

isbn = "9783031463044",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "380--389",

editor = "Huchuan Lu and Risheng Liu and Wanli Ouyang and Hui Huang and Jiwen Lu and Jing Dong and Min Xu",

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address = "Germany",

}

Wang, Y, Pei, M , Nie, Z & Qi, X 2023, Face Anti-spoofing Based on Client Identity Information and Depth Map. in H Lu, R Liu, W Ouyang, H Huang, J Lu, J Dong & M Xu (eds), Image and Graphics - 12th International Conference, ICIG 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14355 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 380-389, 12th International Conference on Image and Graphics, ICIG 2023, Nanjing, China, 22/09/23. https://doi.org/10.1007/978-3-031-46305-1_31

Face Anti-spoofing Based on Client Identity Information and Depth Map. / Wang, Yu; Pei, Mingtao ; Nie, Zhengang et al.
Image and Graphics - 12th International Conference, ICIG 2023, Proceedings. ed. / Huchuan Lu; Risheng Liu; Wanli Ouyang; Hui Huang; Jiwen Lu; Jing Dong; Min Xu. Springer Science and Business Media Deutschland GmbH, 2023. p. 380-389 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14355 LNCS).

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

TY - GEN

T1 - Face Anti-spoofing Based on Client Identity Information and Depth Map

AU - Wang, Yu

AU - Pei, Mingtao

AU - Nie, Zhengang

AU - Qi, Xinmu

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023

Y1 - 2023

N2 - Face anti-spoofing (FAS) is an essential prerequisite for face recognition. In most methods, FAS is usually performed before face recognition and the client identity information is not utilized. Since presentation attacks (PAs) are always aimed at a certain client, the client identity information can provide useful clues for FAS task. In this paper, we propose a face anti-spoofing method based on client identity information using Siamese network. We applied FAS after face recognition to utilize the client identity information. As the real face and fake face have different properties, we use different weights for the two subnetworks of the Siamese network to extract features for real face and fake face, respectively. In addition, we employ depth map as auxiliary information to improve the performance. We perform experiments on SiW, CASIA-FASD and Replay-Attack datasets to demonstrate the validity of our method.

AB - Face anti-spoofing (FAS) is an essential prerequisite for face recognition. In most methods, FAS is usually performed before face recognition and the client identity information is not utilized. Since presentation attacks (PAs) are always aimed at a certain client, the client identity information can provide useful clues for FAS task. In this paper, we propose a face anti-spoofing method based on client identity information using Siamese network. We applied FAS after face recognition to utilize the client identity information. As the real face and fake face have different properties, we use different weights for the two subnetworks of the Siamese network to extract features for real face and fake face, respectively. In addition, we employ depth map as auxiliary information to improve the performance. We perform experiments on SiW, CASIA-FASD and Replay-Attack datasets to demonstrate the validity of our method.

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KW - Depth map

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M3 - Conference contribution

AN - SCOPUS:85177427314

SN - 9783031463044

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

SP - 380

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BT - Image and Graphics - 12th International Conference, ICIG 2023, Proceedings

A2 - Lu, Huchuan

A2 - Liu, Risheng

A2 - Ouyang, Wanli

A2 - Huang, Hui

A2 - Lu, Jiwen

A2 - Dong, Jing

A2 - Xu, Min

PB - Springer Science and Business Media Deutschland GmbH

T2 - 12th International Conference on Image and Graphics, ICIG 2023

Y2 - 22 September 2023 through 24 September 2023

ER -

Wang Y, Pei M , Nie Z, Qi X. Face Anti-spoofing Based on Client Identity Information and Depth Map. In Lu H, Liu R, Ouyang W, Huang H, Lu J, Dong J, Xu M, editors, Image and Graphics - 12th International Conference, ICIG 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 380-389. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-46305-1_31