TY  - JOUR
AU  - Ajian Liu
AU  - Xuan Li
AU  - Jun Wan
AU  - Yanyan Liang
AU  - Sergio Escalera
AU  - Hugo Jair Escalante
AU  - Meysam Madadi
AU  - Yi Jin
AU  - Zhuoyuan Wu
AU  - Xiaogang Yu
AU  - Zichang Tan
AU  - Qi Yuan
AU  - Ruikun Yang
AU  - Benjia Zhou
AU  - Guodong Guo
AU  - Stan Z. Li
PY  - 2020//
TI  - Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review
T2  - BIO
JO  - IET Biometrics
SP  - 24
EP  - 43
VL  - 10
IS  - 1
N2  - Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due 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 CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 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 (IR)) 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 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. 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.
UR  - https://doi.org/10.1049/bme2.12002
L1  - http://refbase.cvc.uab.es/files/LLW2020b.pdf
N1  - HUPBA; no proj
ID  - Ajian Liu2020
ER  -