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

文献类型：期刊论文

作者Liu, Ajian1; Li, Xuan9; Wan, Jun2; Liang, Yanyan1; Escalera, Sergio3,4; Escalante, Hugo Jair5,7; Madadi, Meysam3,4; Jin, Yi9; Wu, Zhuoyuan8; Yu, Xiaogang8
刊名IET BIOMETRICS
出版日期2021
卷号10期号:1页码:24-43
ISSN号2047-4938
DOI10.1049/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.
WOS关键词TEXTURE
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研究方向Computer Science
语种英语
WOS记录号WOS:000603639600002
出版者WILEY
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)
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.Macau Univ Sci & Technol, Fac Informat Technol, Taipa, Macau, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Univ Barcelona, Barcelona, Spain
4.Comp Vis Ctr, Barcelona, Spain
5.Inst Nacl Astrofis Opt & Electr, Puebla, Mexico
6.Inst Deep Learning, Baidu Res & Natl Engn Lab Deep Learning Technol &, Beijing, Peoples R China
7.CINVESTAV Zacatenco, Dept Comp Sci, Mexico City, DF, Mexico
8.Beihang Univ, Sch Software, Beijing, Peoples R China
9.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China
10.Westlake Univ, Hangzhou, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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