Type: Review
Publication Date: 2020-12-15
Citations: 70
DOI: https://doi.org/10.1049/bme2.12002
Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due to the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF cross-ethnicity face anti-spoofing (CeFA), has been released with the goal of measuring the ethnic bias. It is the largest up to date CeFA dataset covering three ethnicities, three modalities, 1607 subjects, 2D plus 3D attack types and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g. RGB) and multi-modal (e.g. RGB, Depth, infrared) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally, 11 and eight teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All of the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This study presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyse the top-ranked solutions and draw conclusions derived from the competition. Besides, we outline future work directions.