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arXiv · 2409.06371

Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition

Junzheng Zhang·Weijia Guo·Bochao Liu·Ruixin Shi·Yong Li·Shiming Ge
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Abstract

Very low-resolution face recognition is challenging due to the serious loss of informative facial details in resolution degradation. In this paper, we propose a generative-discriminative representation distillation approach that combines generative representation with cross-resolution aligned knowledge distillation. This approach facilitates very low-resolution face recognition by jointly distilling generative and discriminative models via two distillation modules. Firstly, the generative representation distillation takes the encoder of a diffusion model pretrained for face super-resolution as the generative teacher to supervise the learning of the student backbone via feature regression, and then freezes the student backbone. After that, the discriminative representation distillation further considers a pretrained face recognizer as the discriminative teacher to supervise the learning of the student head via cross-resolution relational contrastive distillation. In this way, the general backbone representation can be transformed into discriminative head representation, leading to a robust and discriminative student model for very low-resolution face recognition. Our approach improves the recovery of the missing details in very low-resolution faces and achieves better knowledge transfer. Extensive experiments on face datasets demonstrate that our approach enhances the recognition accuracy of very low-resolution faces, showcasing its effectiveness and adaptability.

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Junzheng Zhang, Weijia Guo, Bochao Liu, Ruixin Shi, Yong Li, Shiming Ge. 2024-09-10. Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition. https://arxiv.org/abs/2409.06371

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