Cross‐ethnicity face anti‐spoofing recognition challenge: A review
Abstract 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 verifi...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | IET Biometrics |
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| Online Access: | https://doi.org/10.1049/bme2.12002 |
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| author | Ajian Liu Xuan Li Jun Wan Yanyan Liang Sergio Escalera Hugo Jair Escalante Meysam Madadi Yi Jin Zhuoyuan Wu Xiaogang Yu Zichang Tan Qi Yuan Ruikun Yang Benjia Zhou Guodong Guo Stan Z. Li |
| author_facet | Ajian Liu Xuan Li Jun Wan Yanyan Liang Sergio Escalera Hugo Jair Escalante Meysam Madadi Yi Jin Zhuoyuan Wu Xiaogang Yu Zichang Tan Qi Yuan Ruikun Yang Benjia Zhou Guodong Guo Stan Z. Li |
| author_sort | Ajian Liu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-05dbf04b9906481f9beb2105e6e4b3ef |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-05dbf04b9906481f9beb2105e6e4b3ef2025-02-03T01:29:37ZengWileyIET Biometrics2047-49382047-49462021-01-01101244310.1049/bme2.12002Cross‐ethnicity face anti‐spoofing recognition challenge: A reviewAjian Liu0Xuan Li1Jun Wan2Yanyan Liang3Sergio Escalera4Hugo Jair Escalante5Meysam Madadi6Yi Jin7Zhuoyuan Wu8Xiaogang Yu9Zichang Tan10Qi Yuan11Ruikun Yang12Benjia Zhou13Guodong Guo14Stan Z. Li15Faculty of Information Technology Macau University of Science and Technology Taipa Macau ChinaSchool of Computer and Information Technology Beijing Jiaotong University Beijing ChinaNational Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing ChinaFaculty of Information Technology Macau University of Science and Technology Taipa Macau ChinaUniversitat de Barcelona and Computer Vision Center Barcelona SpainInstituto Nacional de Astrofísica, Óptica y Electrónica Puebla MexicoUniversitat de Barcelona and Computer Vision Center Barcelona SpainSchool of Computer and Information Technology Beijing Jiaotong University Beijing ChinaSchool of Software Beihang University Beijing ChinaSchool of Software Beihang University Beijing ChinaNational Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing ChinaSchool of Software Beihang University Beijing ChinaFaculty of Information Technology Macau University of Science and Technology Taipa Macau ChinaFaculty of Information Technology Macau University of Science and Technology Taipa Macau ChinaBaidu Research and National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Beijing ChinaFaculty of Information Technology Macau University of Science and Technology Taipa Macau ChinaAbstract 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.https://doi.org/10.1049/bme2.12002biometrics (access control)face recognitionprejudicial factorsdeep learning (artificial intelligence) |
| spellingShingle | Ajian Liu Xuan Li Jun Wan Yanyan Liang Sergio Escalera Hugo Jair Escalante Meysam Madadi Yi Jin Zhuoyuan Wu Xiaogang Yu Zichang Tan Qi Yuan Ruikun Yang Benjia Zhou Guodong Guo Stan Z. Li Cross‐ethnicity face anti‐spoofing recognition challenge: A review IET Biometrics biometrics (access control) face recognition prejudicial factors deep learning (artificial intelligence) |
| title | Cross‐ethnicity face anti‐spoofing recognition challenge: A review |
| title_full | Cross‐ethnicity face anti‐spoofing recognition challenge: A review |
| title_fullStr | Cross‐ethnicity face anti‐spoofing recognition challenge: A review |
| title_full_unstemmed | Cross‐ethnicity face anti‐spoofing recognition challenge: A review |
| title_short | Cross‐ethnicity face anti‐spoofing recognition challenge: A review |
| title_sort | cross ethnicity face anti spoofing recognition challenge a review |
| topic | biometrics (access control) face recognition prejudicial factors deep learning (artificial intelligence) |
| url | https://doi.org/10.1049/bme2.12002 |
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