3D High-Fidelity Mask Face Presentation Attack Detection Challenge
This work addresses security vulnerabilities in face recognition for biometric systems, but it is incremental as it builds on existing research by providing a new dataset and competition framework.
The paper tackles the threat of 3D masks to face recognition systems by organizing a challenge using a new large-scale dataset (CASIA-SURF HiFiMask with 54,600 videos) and a protocol to evaluate algorithm discrimination and generalization, resulting in 195 teams participating and 18 qualifying for the final round.
The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of a total amount of 54, 600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask-based attack detection. It attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranking algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition.