research-article

An Improved GAN-based Depth Estimation Network for Face Anti-Spoofing

Authors:

Ping WangAuthors Info & Claims

ICCAI '23: Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence

Pages 323 - 328

https://doi.org/10.1145/3594315.3594661

Published: 02 August 2023 Publication History

Abstract

Face spoofing is a serious threat to the security of face recognition and face anti-spoofing (FAS) has become one of the popular research directions recently. The pipeline of modern face anti-spoofing methods is to train deep neural networks to distinguish live and spoof faces using various auxiliary information. Depth maps are the most commonly used auxiliary features due to their computational simplicity and ease of discrimination. However, existing methods do not generalize well in complex environments and against unknown attacks. In this paper, we propose a novel FAS method using generative adversarial network (GAN). We train a GAN for generating depth maps of faces. The network uses the architecture of Wasserstein GAN and adds an attention module to learn the contribution of different regions to the deep graph. The classifier is trained using a latent variable containing depth information in the network. Experiments demonstrate that our approach can better generalize complex external conditions such as illumination and background. Among all results, we gain the best result of 1.17% (EER) on CASIA-FASD and 21.57% (HTER) on cross testing between Replay-Attack and CASIA-FASD.

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Cited By

Antil ADhiman C(2025)Unmasking Deception: A Comprehensive Survey on the Evolution of Face Anti‐spoofing MethodsNeurocomputing10.1016/j.neucom.2024.128992617(128992)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128992
Antil ADhiman C(2024)Securing Faces: A GAN-Powered Defense Against Spoofing with MSRCR and CBAMPattern Recognition10.1007/978-3-031-78201-5_28(430-449)Online publication date: 2-Dec-2024
https://doi.org/10.1007/978-3-031-78201-5_28

Index Terms

An Improved GAN-based Depth Estimation Network for Face Anti-Spoofing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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ICCAI '23: Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence

March 2023

824 pages

ISBN:9781450399029

DOI:10.1145/3594315

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 August 2023

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ICCAI 2023

ICCAI 2023: 2023 9th International Conference on Computing and Artificial Intelligence

March 17 - 20, 2023

Tianjin, China

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Cited By

Antil ADhiman C(2025)Unmasking Deception: A Comprehensive Survey on the Evolution of Face Anti‐spoofing MethodsNeurocomputing10.1016/j.neucom.2024.128992617(128992)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128992
Antil ADhiman C(2024)Securing Faces: A GAN-Powered Defense Against Spoofing with MSRCR and CBAMPattern Recognition10.1007/978-3-031-78201-5_28(430-449)Online publication date: 2-Dec-2024
https://doi.org/10.1007/978-3-031-78201-5_28

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