Efficient Low-Resolution Face Recognition via Bridge Distillation

By Shiming Ge, Shengwei Zhao, Chenyu Li, Yu Zhang, Jia Li. 2020

72 citations recorded by the source corpus.

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Knowledge Distillation: A Survey (2020, methodology): “…recognition accuracy, the knowledge distillation framework is developed by using architectures between high-resolution face teacher and low-resolution face student for model acceleration and improved classiﬁcation performance (Ge et al. 2018; Wang et al. 2019c; Kong et al. 2019; Ge et al. 2020).”