University of Oulu

Z. Yu, Y. Qin, X. Li, C. Zhao, Z. Lei and G. Zhao, "Deep Learning for Face Anti-Spoofing: A Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 5, pp. 5609-5631, 1 May 2023, doi: 10.1109/TPAMI.2022.3215850

Deep learning for face anti-spoofing : a survey

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Author: Yu, Zitong1; Qin, Yunxiao2; Li, Xiaobai1;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
2State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
3SailYond Technology, Beijing, China
4National Laboratory of Pattern Recognition (NLPR), Center for Biometrics and Security Research (CBSR), Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China
5School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS), Beijing, China
6Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, SAR
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-05-26


Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, early-stage FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., ‘0’ for bonafide versus ‘1’ for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.

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Series: IEEE transactions on pattern analysis and machine intelligence
ISSN: 0162-8828
ISSN-E: 2160-9292
ISSN-L: 0162-8828
Volume: 45
Issue: 5
Pages: 5609 - 5631
DOI: 10.1109/TPAMI.2022.3215850
Type of Publication: A2 Review article in a scientific journal
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Academy of Finland (Academy Professor project EmotionAI) under Grants 336116, 345122, ICT2023, and 345948, in part by the Chinese National Natural Science Foundation Projects under Grants 62276254, 61976229, and 62106264 and in part by the InnoHK program, and Beijing Academy of Artificial Intelligence (BAAI).
Academy of Finland Grant Number: 336116
Detailed Information: 336116 (Academy of Finland Funding decision)
345122 (Academy of Finland Funding decision)
345948 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2022. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see