Cross-ethnicity face anti-spoofing recognition challenge: A review
Liu, Ajian1; Li, Xuan2; Wan, Jun3; Liang, Yanyan1; Escalera, Sergio4,5; Escalante, Hugo Jair6,7; Madadi, Meysam4,5; Jin, Yi2; Wu, Zhuoyuan8; Yu, Xiaogang8; Tan, Zichang3; Yuan, Qi8; Yang, Ruikun1; Zhou, Benjia1; Guo, Guodong9; Li, Stan Z.1,3,10
发表期刊IET BIOMETRICS
ISSN2047-4938
2021
卷号10期号:1页码:24-43
通讯作者Wan, Jun(jun.wan@ia.ac.cn)
摘要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.
DOI10.1049/bme2.12002
关键词[WOS]TEXTURE
收录类别SCI
语种英语
资助项目Chinese National Natural Science Foundation Projects ; Science and Technology Development Fund of Macau[0025/2018/A1] ; Science and Technology Development Fund of Macau[0008/2019/A1] ; Science and Technology Development Fund of Macau[0019/2018/ASC] ; Science and Technology Development Fund of Macau[0010/2019/AFJ] ; Science and Technology Development Fund of Macau[0025/2019/AKP] ; Key Project of the General Logistics Department[ASW17C001] ; Spanish project (MINECO/FEDER, UE)[PID2019-105093GB-I00]
项目资助者Chinese National Natural Science Foundation Projects ; Science and Technology Development Fund of Macau ; Key Project of the General Logistics Department ; Spanish project (MINECO/FEDER, UE)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000603639600002
出版者WILEY
七大方向——子方向分类生物特征识别
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/42533
专题模式识别国家重点实验室_生物识别与安全技术
通讯作者Wan, Jun
作者单位1.Macau Univ Sci & Technol, Fac Informat Technol, Taipa, Macau, Peoples R China
2.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
4.Univ Barcelona, Barcelona, Spain
5.Comp Vis Ctr, Barcelona, Spain
6.Inst Nacl Astrofis Opt & Electr, Puebla, Mexico
7.CINVESTAV Zacatenco, Dept Comp Sci, Mexico City, DF, Mexico
8.Beihang Univ, Sch Software, Beijing, Peoples R China
9.Inst Deep Learning, Baidu Res & Natl Engn Lab Deep Learning Technol &, Beijing, Peoples R China
10.Westlake Univ, Hangzhou, Peoples R China
通讯作者单位模式识别国家重点实验室
推荐引用方式
GB/T 7714
Liu, Ajian,Li, Xuan,Wan, Jun,et al. Cross-ethnicity face anti-spoofing recognition challenge: A review[J]. IET BIOMETRICS,2021,10(1):24-43.
APA Liu, Ajian.,Li, Xuan.,Wan, Jun.,Liang, Yanyan.,Escalera, Sergio.,...&Li, Stan Z..(2021).Cross-ethnicity face anti-spoofing recognition challenge: A review.IET BIOMETRICS,10(1),24-43.
MLA Liu, Ajian,et al."Cross-ethnicity face anti-spoofing recognition challenge: A review".IET BIOMETRICS 10.1(2021):24-43.
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