University of Oulu

Yu Z., Li X., Niu X., Shi J., Zhao G. (2020) Face Anti-Spoofing with Human Material Perception. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12352. Springer, Cham.

Face anti-spoofing with human material perception

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Author: Yu, Zitong1; Li, Xiaobai1; Niu, Xuesong2,3;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
2Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China
3University of Chinese Academy of Sciences, China
4School of Software Engineering, Xian Jiaotong University, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 9.3 MB)
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Language: English
Published: Springer Nature, 2020
Publish Date: 2021-02-04


Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems from presentation attacks. Most existing FAS methods capture various cues (e.g., texture, depth and reflection) to distinguish the live faces from the spoofing faces. All these cues are based on the discrepancy among physical materials (e.g., skin, glass, paper and silicone). In this paper we rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception, intending to extract discriminative and robust features for FAS. To this end, we propose the Bilateral Convolutional Networks (BCN), which is able to capture intrinsic material-based patterns via aggregating multi-level bilateral macro- and micro- information. Furthermore, Multi-level Feature Refinement Module (MFRM) and multi-head supervision are utilized to learn more robust features. Comprehensive experiments are performed on six benchmark datasets, and the proposed method achieves superior performance on both intra- and cross-dataset testings. One highlight is that we achieve overall 11.3 ± 9.5% EER for cross-type testing in SiW-M dataset, which significantly outperforms previous results. We hope this work will facilitate future cooperation between FAS and material communities.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-030-58571-6
ISBN Print: 978-3-030-58570-9
Pages: 557 - 575
DOI: 10.1007/978-3-030-58571-6_33
Host publication: Computer Vision – ECCV 2020 16th European Conference Proceedings, Part VII, Glasgow, UK, August 23–28, 2020
Host publication editor: Vedaldi, Andrea
Bischof, Horst
Brox, Thomas
Frahm, Jan-Michael
Conference: European Conference on Computer Vision
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This work was supported by the Academy of Finland for project MiGA (grant 316765), ICT 2023 project (grant 328115), and Infotech Oulu. We also acknowledge CSC-IT Center for Science, Finland, for computational resources.
Academy of Finland Grant Number: 316765
Detailed Information: 316765 (Academy of Finland Funding decision)
328115 (Academy of Finland Funding decision)
Copyright information: © Springer Nature Switzerland AG 2020. This is a post-peer-review, pre-copyedit version of an article published in Computer Vision – ECCV 2020 16th European Conference Proceedings, Part VII, Glasgow, UK, August 23–28, 2020. The final authenticated version is available online at: