Cross-ethnicity face anti-spoofing recognition challenge: A review

doi:10.1049/bme2.12002

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 生物识别与安全技术研究中心

	Cross-ethnicity face anti-spoofing recognition challenge: A review
	Liu, Ajian 2; Li, Xuan 3; Wan, Jun 4; Liang, Yanyan 2; Escalera, Sergio 5,6; Escalante, Hugo Jair 1,10; Madadi, Meysam 5,6; Jin, Yi 3; Wu, Zhuoyuan 7; Yu, Xiaogang 7
刊名	IET BIOMETRICS
	2021
卷号	10 期号:1 页码:24-43
ISSN号	2047-4938
DOI	10.1049/bme2.12002
通讯作者	Wan, Jun(jun.wan@ia.ac.cn)
英文摘要	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.
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]
WOS关键词	TEXTURE
WOS研究方向	Computer Science
语种	英语
出版者	WILEY
WOS记录号	WOS:000603639600002
资助机构	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)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42532]
专题	自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者	Wan, Jun
作者单位	1.Inst Nacl Astrofis Opt & Electr, Puebla, Mexico 2.Macau Univ Sci & Technol, Fac Informat Technol, Taipa, Macau, Peoples R China 3.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 5.Univ Barcelona, Barcelona, Spain 6.Comp Vis Ctr, Barcelona, Spain 7.Beihang Univ, Sch Software, Beijing, Peoples R China 8.Westlake Univ, Hangzhou, Peoples R China 9.Inst Deep Learning, Baidu Res & Natl Engn Lab Deep Learning Technol &, Beijing, Peoples R China 10.CINVESTAV Zacatenco, Dept Comp Sci, Mexico City, DF, Mexico
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.