  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.
