Single Patch Based 3D High-Fidelity Mask Face Anti-Spoofing

Face anti-spoofing is rapidly increasing in importance as facial recognition systems have become common in the financial and security fields. Among all kinds of attack, 3D high-fidelity masks are especially hard to defend. Recently, CASIA introduced a large scale dataset CASIA-SURF HiFiMask, which c...

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Bibliographic Details
Published inIEEE International Conference on Computer Vision workshops pp. 842 - 845
Main Authors Huang, Samuel, Cheng, Wen-Huang, Cheng, Robert
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2021
Subjects
Computer vision
Conferences
Error analysis
Face recognition
Protocols
Solid modeling
Three-dimensional displays
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Summary:Face anti-spoofing is rapidly increasing in importance as facial recognition systems have become common in the financial and security fields. Among all kinds of attack, 3D high-fidelity masks are especially hard to defend. Recently, CASIA introduced a large scale dataset CASIA-SURF HiFiMask, which comprises of 54,600 videos recorded from 75 subjects with 225 high-fidelity masks. In this paper, we design a lightweight network with single patch input on the basis of CDCN++, and supervise it by focal loss. The proposed method achieves the Average Classification Error Rate (ACER) of 3.215 on the Protocol 3 of CASIASURF HiFiMask dataset and ranks the third best model in the Chalearn 3D High-Fidelity Mask Face Presentation Attack Detection Challenge at ICCV 2021.
ISSN:2473-9944
DOI:10.1109/ICCVW54120.2021.00099