Knowledge Commons of Institute of Automation,CAS
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 |
ISSN | 2047-4938 |
2021 | |
卷号 | 10期号:1页码:24-43 |
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. |
DOI | 10.1049/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 |
国重实验室规划方向分类 | 人工智能基础前沿理论 |
是否有论文关联数据集需要存交 | 否 |
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 | 多模态人工智能系统全国重点实验室_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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