DANet: Dynamic Attention to Spoof Patterns for Face Anti-Spo…
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Work Details
- DOI 10.1109/icpr56361.2022.9956725
- OpenAlex ID W4312792082
- Type Conference-paper
- Language EN
- Published 21 Aug 2022
- Pages 1929–1936
- Source Proceedings - International Conference o…
- FWCI 1.24
- Countries 1
- Institutions 1
- Added 05 Jan 2023
- Updated 16 Oct 2026
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DANet: Dynamic Attention to Spoof Patterns for Face Anti-Spoofing
Conference-paper 2022 EN
Face anti-spoofing is a vital part to protect the security of face recognition systems. Many existing face anti-spoofing methods rely on convolutional neural networks (CNNs) and achieve competitive performance. However, due to the power of CNNs, these methods will extract information that is irrelevant to spoof patterns, such as acquisition equipment and environmental characteristics, which makes…
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1.24
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Authors & Affiliations
5 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Chunyu Sun | University of Science and Tech… (CN) | — |
| 2 | Song-Lu Chen | University of Science and Tech… (CN) | |
| 3 | Xinjie Li | University of Science and Tech… (CN) | |
| 4 | Feng Chen | University of Science and Tech… (CN) | |
| 5 | Xu-Cheng Yin | University of Science and Tech… (CN) |
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