CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results

As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof....

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Published in:arXiv.org 2021-02
Main Authors: Zhang, Yuanhan, Yin, Zhenfei, Shao, Jing, Liu, Ziwei, Yang, Shuo, Xiong, Yuanjun, Xia, Wei, Xu, Yan, Luo, Man, Liu, Jian, Li, Jianshu, Chen, Zhijun, Guo, Mingyu, Li, Hui, Liu, Junfu, Gao, Pengfei, Hong, Tianqi, Han, Hao, Liu, Shijie, Chen, Xinhua, Qiu, Di, Cheng, Zhen, Liang, Dashuang, Jin, Yufeng, Zhanlong Hao
Format: Article
Language:English
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Datasets
Spoofing
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Summary:As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Recently, a large-scale face anti-spoofing dataset, CelebA-Spoof which comprised of 625,537 pictures of 10,177 subjects has been released. It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects. This paper reports methods and results in the CelebA-Spoof Challenge 2020 on Face AntiSpoofing which employs the CelebA-Spoof dataset. The model evaluation is conducted online on the hidden test set. A total of 134 participants registered for the competition, and 19 teams made valid submissions. We will analyze the top ranked solutions and present some discussion on future work directions.
ISSN:2331-8422