Out of Domain Face Anti-spoofing: A Survey
Face anti-spoofing (FAS), as an important means to protect face recognition models, can ensure that the system remains secure and reliable in the face of various presentation attacks. The current deep learning-based face anti-spoofing model has satisfactory results when the test data and training da...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2022-11-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2203082.pdf |
| _version_ | 1811308357499224064 |
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| author | SHI Yichen, FENG Jun, XIAO Lixuan, HE Jingjing, HU Jingjing |
| author_facet | SHI Yichen, FENG Jun, XIAO Lixuan, HE Jingjing, HU Jingjing |
| author_sort | SHI Yichen, FENG Jun, XIAO Lixuan, HE Jingjing, HU Jingjing |
| collection | DOAJ |
| description | Face anti-spoofing (FAS), as an important means to protect face recognition models, can ensure that the system remains secure and reliable in the face of various presentation attacks. The current deep learning-based face anti-spoofing model has satisfactory results when the test data and training data obey the same distribution, but the accuracy of the model decreases considerably when the trained model infers in the scene outside the domain, such as cross-domain transfer and out-of-distribution scenarios. The problems that silent face anti-spoofing models will encounter in real scenarios, i.e., the models encounter unknown environments and unknown attack methods, are mainly described. The corresponding solutions are classified into four categories: methods based on domain adaptation, methods based on domain generalization, methods based on zero shot or few shot learning, and methods based on anomaly detection. Each solution and its deep learning model methods are summarized and compared. The mechanism, network structure, advantages, limitations and application scenarios of some major methods are summarized. After that, common public datasets, evaluation metrics, measurement protocols commonly used for face anti-spoofing in out of domain scenarios and test results of state-of-the-art methods under some protocols are introduced. Finally, the difficulties and challenges of face anti-spoofing in practical applications are discussed, and future research directions are summarized. |
| first_indexed | 2024-04-13T09:20:40Z |
| format | Article |
| id | doaj.art-0edc0963271c45928bc4630f68d72328 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-13T09:20:40Z |
| publishDate | 2022-11-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-0edc0963271c45928bc4630f68d723282022-12-22T02:52:35ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182022-11-0116112471248610.3778/j.issn.1673-9418.2203082Out of Domain Face Anti-spoofing: A SurveySHI Yichen, FENG Jun, XIAO Lixuan, HE Jingjing, HU Jingjing01. School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaFace anti-spoofing (FAS), as an important means to protect face recognition models, can ensure that the system remains secure and reliable in the face of various presentation attacks. The current deep learning-based face anti-spoofing model has satisfactory results when the test data and training data obey the same distribution, but the accuracy of the model decreases considerably when the trained model infers in the scene outside the domain, such as cross-domain transfer and out-of-distribution scenarios. The problems that silent face anti-spoofing models will encounter in real scenarios, i.e., the models encounter unknown environments and unknown attack methods, are mainly described. The corresponding solutions are classified into four categories: methods based on domain adaptation, methods based on domain generalization, methods based on zero shot or few shot learning, and methods based on anomaly detection. Each solution and its deep learning model methods are summarized and compared. The mechanism, network structure, advantages, limitations and application scenarios of some major methods are summarized. After that, common public datasets, evaluation metrics, measurement protocols commonly used for face anti-spoofing in out of domain scenarios and test results of state-of-the-art methods under some protocols are introduced. Finally, the difficulties and challenges of face anti-spoofing in practical applications are discussed, and future research directions are summarized.http://fcst.ceaj.org/fileup/1673-9418/PDF/2203082.pdf|face anti-spoofing (fas)|domain adaptation|domain generalization|zero shot/few shot learning|anomaly detection|deep learning |
| spellingShingle | SHI Yichen, FENG Jun, XIAO Lixuan, HE Jingjing, HU Jingjing Out of Domain Face Anti-spoofing: A Survey Jisuanji kexue yu tansuo |face anti-spoofing (fas)|domain adaptation|domain generalization|zero shot/few shot learning|anomaly detection|deep learning |
| title | Out of Domain Face Anti-spoofing: A Survey |
| title_full | Out of Domain Face Anti-spoofing: A Survey |
| title_fullStr | Out of Domain Face Anti-spoofing: A Survey |
| title_full_unstemmed | Out of Domain Face Anti-spoofing: A Survey |
| title_short | Out of Domain Face Anti-spoofing: A Survey |
| title_sort | out of domain face anti spoofing a survey |
| topic | |face anti-spoofing (fas)|domain adaptation|domain generalization|zero shot/few shot learning|anomaly detection|deep learning |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2203082.pdf |
| work_keys_str_mv | AT shiyichenfengjunxiaolixuanhejingjinghujingjing outofdomainfaceantispoofingasurvey |