Abstract
Low-resolution face recognition is a challenging task due to the missing of informative details. Recent approaches based on knowledge distillation have proven that high-resolution clues can well guide low-resolution face recognition via proper knowledge transfer. However, due to the distribution difference between training and testing faces, the learned models often suffer from poor adaptability. To address that, we split the knowledge transfer process into distillation and adaptation steps, and propose an adaptable instance-relation distillation approach to facilitate low-resolution face recognition. In the approach, the student distills knowledge from high-resolution teacher in both instance level and relation level, providing sufficient cross-resolution knowledge transfer. Then, the learned student can be adaptable to recognize low-resolution faces with adaptive batch normalization in inference. In this manner, the capability of recovering missing details of familiar low-resolution faces can be effectively enhanced, leading to a better knowledge transfer. Extensive experiments on low-resolution face recognition clearly demonstrate the effectiveness and adaptability of our approach.
Abstract (translated)
低分辨率面部识别是一个具有挑战性的任务，因为缺少了有用的细节信息。基于知识蒸馏的最近方法证明，高分辨率的信息可以通过适当的知识传递来引导低分辨率面部识别。然而，由于训练和测试面部的分布差异，学习到的模型通常会出现 poor adaptability（不适应性）。为了解决这个问题，我们将知识传递过程分为蒸馏和适应步骤，并提出了一种适应性实例关系蒸馏方法，以促进低分辨率面部识别。在方法中，学生从高分辨率教师在实例级别和关系级别上提取知识，提供足够的跨分辨率知识传递。然后，学习到的学生可以在推理中适应性地识别低分辨率面部。这种方式可以有效地增强恢复熟悉低分辨率面部的缺失细节，从而提高知识传递。在低分辨率面部识别方面进行的大量实验充分证明了我们的方法的有效性和可适应性。
URL
https://arxiv.org/abs/2409.02049
