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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3091167
|2 doi
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|a pubmed25n1089.xml
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|a (DE-627)NLM327042915
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|a (NLM)34156938
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Qin, Yunxiao
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Meta-Teacher For Face Anti-Spoofing
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 16.09.2022
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|a Date Revised 19.11.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Face anti-spoofing (FAS) secures face recognition from presentation attacks (PAs). Existing FAS methods usually supervise PA detectors with handcrafted binary or pixel-wise labels. However, handcrafted labels may are not the most adequate way to supervise PA detectors learning sufficient and intrinsic spoofing cues. Instead of using the handcrafted labels, we propose a novel Meta-Teacher FAS (MT-FAS) method to train a meta-teacher for supervising PA detectors more effectively. The meta-teacher is trained in a bi-level optimization manner to learn the ability to supervise the PA detectors learning rich spoofing cues. The bi-level optimization contains two key components: 1) a lower-level training in which the meta-teacher supervises the detector's learning process on the training set; and 2) a higher-level training in which the meta-teacher's teaching performance is optimized by minimizing the detector's validation loss. Our meta-teacher differs significantly from existing teacher-student models because the meta-teacher is explicitly trained for better teaching the detector (student), whereas existing teachers are trained for outstanding accuracy neglecting teaching ability. Extensive experiments on five FAS benchmarks show that with the proposed MT-FAS, the trained meta-teacher 1) provides better-suited supervision than both handcrafted labels and existing teacher-student models; and 2) significantly improves the performances of PA detectors
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Yu, Zitong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Longbin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Zezheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Chenxu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lei, Zhen
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 10 vom: 22. Okt., Seite 6311-6326
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
1 |
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|g volume:44
|g year:2022
|g number:10
|g day:22
|g month:10
|g pages:6311-6326
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2021.3091167
|3 Volltext
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|d 44
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