University of Oulu

Z. Yu et al., "Multi-Modal Face Anti-Spoofing Based on Central Difference Networks," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2020, pp. 2766-2774, doi: 10.1109/CVPRW50498.2020.00333

Multi-modal face anti-spoofing based on central difference networks

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Author: Yu, Zitong1; Qin, Yunxiao2; Li, Xiaobai1;
Organizations: 1CMVS, University of Oulu
2Northwestern Polytechnical University
3Mininglamp Academy of Sciences, Mininglamp Technology
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 4.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-19


Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, which is weak in describing detailed intrinsic information from modalities and easily being ineffective when the domain shifts (e.g., cross attack and cross ethnicity). In this paper, we extend the central difference convolutional networks (CDCN) [39] to a multimodal version, intending to capture intrinsic spoofing patterns among three modalities (RGB, depth and infrared). Meanwhile, we also give an elaborate study about singlemodal based CDCN. Our approach won the first place in "Track Multi-Modal" as well as the second place in “Track Single-Modal (RGB)” of ChaLearn Face Antispoofing Attack Detection Challenge@CVPR2020 [20]. Our final submission obtains 1.02±0.59% and 4.84±1.79% ACER in “Track Multi-Modal” and “Track Single-Modal (RGB)”, respectively. The codes are available at

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Series: IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops
ISSN: 2160-7508
ISSN-E: 2160-7516
ISSN-L: 2160-7508
ISBN: 978-1-7281-9360-1
ISBN Print: 978-1-7281-9361-8
Pages: 2766 - 2774
Article number: 9150999
DOI: 10.1109/CVPRW50498.2020.00333
Host publication: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the Academy of Finland for project MiGA (Grant 316765), ICT 2023 project (Grant 328115), Infotech Oulu and the Chinese National Natural Science Foundation Projects (Grant No. 61876178). As well, the authors acknowledge CSCIT 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)
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