CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
This addresses security in facial interaction systems by benchmarking methods on a new dataset, but it is incremental as it focuses on reporting competition results without introducing new techniques.
The paper reports on the CelebA-Spoof Challenge 2020, which tackled face anti-spoofing using a large-scale dataset of 625,537 images from 10,177 subjects, with 134 participants and 19 teams submitting solutions.
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.