Efficient Low-Resolution Face Recognition via Bridge Distillation
<|reference_start|>Efficient Low-Resolution Face Recognition via Bridge Distillation: 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.<|reference_end|>
@article{ge2024efficient,
    title={Efficient Low-Resolution Face Recognition via Bridge Distillation},
    author={Shiming Ge and Shengwei Zhao and Chenyu Li and Yu Zhang and Jia Li},
    journal={IEEE TIP 2020},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11786},
    primaryClass={cs.CV cs.AI cs.MM}
}