期刊
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2019)
卷 -, 期 -, 页码 1601-1610出版社
IEEE
DOI: 10.1109/CVPRW.2019.00202
关键词
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资金
- Science and Technology Development Fund of Macau [0025/2018/A1]
- Chinese National Natural Science Foundation [61876179, 61872367]
- Spanish project (MINECO/FEDER, UE) [TIN2016-74946-P]
- CERCA Programme/Generalitat de Catalunya
- ICREA under the ICREA Academia programme
Anti-spoofing attack detection is critical to guarantee the security of face-based authentication and facial analysis systems. Recently, a multi-modalface anti-spoofing dataset, CASIA-SURF, has been released with the goal of boosting research in this important topic. CASIA-SURF is the largest public data set for facial anti-spoofing attack detection in terms of both, diversity and modalities: it comprises 1,000 subjects and 21, 000 video samples. We organized a challenge around this novel resource to boost research in the subject. The Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.
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