Domain Adaptation for Face Anti-Spoofing Based on Dual Disentanglement and Liveness Feature Progressive Alignment

Feng Jun; Shi Yichen; Gao Yuhao; He Jingjing; Yu Zitong

doi:10.7544/issn1000-1239.202330251

Journal of Computer Research and Development > 2023 > 60(8): 1727-1739. > DOI: 10.7544/issn1000-1239.202330251

Feng Jun, Shi Yichen, Gao Yuhao, He Jingjing, Yu Zitong. Domain Adaptation for Face Anti-Spoofing Based on Dual Disentanglement and Liveness Feature Progressive Alignment[J]. Journal of Computer Research and Development, 2023, 60(8): 1727-1739. DOI: 10.7544/issn1000-1239.202330251

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Domain Adaptation for Face Anti-Spoofing Based on Dual Disentanglement and Liveness Feature Progressive Alignment

1.
School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043
2.
Great Bay University, Dongguan, Guangdong 523000

Funds: This work was supported by the National Natural Science Foundation of China (61772070, 61972267) and the Key Projects of Science and Technology Research in Colleges and Universities of Hebei Province (ZD2021333).

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Author Bio:
Feng Jun: born in 1971. PhD, professor. Member of CCF. Her main research interests include computer vision, machine learning, and complex network analysis

Shi Yichen: born in 1998. Master. His main research interests include face anti-spoofing and transfer learning

Gao Yuhao: born in 2000. Master candidate. His main research interest includes face anti-spoofing

He Jingjing: born in 2000. Master candidate. Her main research interest includes face anti-spoofing

Yu Zitong: born in 1992. PhD, assistant professor. His main research interests include biometric recognition and multimedia security
Received Date: March 30, 2023
Revised Date: June 15, 2023
Available Online: June 26, 2023

Graphical Abstract

Abstract

Abstract

Although existing face anti-spoofing methods perform well in intra-domain testing, their performance significantly degrades in cross-domain scenarios. Current cross-domain face anti-spoofing methods based on domain adversarial alignment cannot guarantee that the alignment task directly serves the classification task since the alignment and classification networks are independent of each other. We propose a domain adaptation for face anti-spoofing method based on domain invariant liveness features dual disentanglement and progressive adversarial alignment. Firstly, the source domain features are heuristically disentangled into domain specific features and domain invariant features. Then, the gradient of classifier is used to perform a second disentanglement of the live-related and live-unrelated features in the domain invariant features. To alleviate optimization difficulties during training, a curriculum learning method is adopted to progressively align target domain features and the combination of live-related and live-unrelated features, gradually increasing the proportion of live-related features, and enhancing the correlation between the target domain features and face anti-spoofing task. From a causal perspective, we provide an explanation for the liveness alignment domain adaptation. Experimental results on CASIA-MFSD, Idiap Replay-Attack, MSU-MFSD, and OULU-NPU datasets demonstrate that the proposed method achieves the best average HTER value of 22.5% compared with ten existing methods and the current state-of-the-art performance on four evaluation protocols. Especially the HTER values of I-M and O-M evaluation protocols achieve 12.4% and 12.8%, respectively. The proposed method can significantly reduce the error rates of the model in the target domain and has better cross-domain generalization ability.
- face anti-spoofing,
- domain adaptation,
- dual disentanglement,
- domain adversarial progressive alignment,
- curriculum learning

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References (51)

References

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