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

Samuel Huang, Wen-Huang Cheng, Robert Cheng

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面842-845
頁數4
ISBN(電子)9781665401913
DOIs
出版狀態Published - 2021
事件18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, 加拿大
持續時間: 11 10月 202117 10月 2021

出版系列

名字Proceedings of the IEEE International Conference on Computer Vision
2021-October
ISSN(列印)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
國家/地區加拿大
城市Virtual, Online
期間11/10/2117/10/21

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