Abstract
Face recognition (FR) is one of the most successful image analysis and understanding applications, which has recently received significant attentions. However, despite the remarkable progress in face recognition-related technologies, dependably recognizing surveillance faces is still a big challenge due to different data distributions between commonly used faces and surveillance faces. Although collecting labeled surveillance faces could be direct and helpful, it is infeasible due to privacy and labor cost. In comparison, it is practical to use the unsupervised domain adaptation (UDA) method to regularize the learning of surveillance face representations by utilizing small-scale unlabeled data. In this paper, a joint pixel-level and feature-level UDA framework has been proposed for tacking the above problems. Specifically, we introduce a training method of domain adversarial to align the domain gap in feature space. Additionally, we propose to use a CycleGAN-based image style transformer to capture pixel-level domain shifts. Moreover, we adopt feature identity-consistency loss on the original CycleGAN to alleviate the identity attribute shift caused by unstable image style transfer. The experimental results on the self-built SFace-450 dataset demonstrate that our method consistently outperforms the state-of-the-art FR and UDA methods.
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Acknowledgement
This work is supported by Industry-University Cooperation Project of Fujian Science and Technology Department (No. 2021H6035), and the Science and Technology Planning Project of Fujian Province (No. 2021J011182, 2020H0023, 2020Y9064), and the Joint Funds of 5th Round of Health and Education Research Program of Fujian Province (No. 2019-WJ-41).
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Zhu, H., Yin, H., Xia, D., Wang, DH., Liu, X., Zhu, S. (2022). Joint Pixel-Level and Feature-Level Unsupervised Domain Adaptation for Surveillance Face Recognition. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_36
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