In this study, we introduce a feature knowledge distillation framework to improve low-resolution (LR) face recognition performance using knowledge obtained from high-resolution (HR) images. The proposed framework transfers informative features from an HR-trained network to an LR-trained network by reducing the distance between them. A cosine similarity measure was employed as a distance metric to effectively align the HR and LR features. This approach differs from conventional knowledge distillation frameworks, which use the L_p distance metrics and offer the advantage of converging well when reducing the distance between features of different resolutions. Our framework achieved a 3% improvement over the previous state-of-the-art method on the AgeDB-30 benchmark without bells and whistles, while maintaining a strong performance on HR images. The effectiveness of cosine similarity as a distance metric was validated through statistical analysis, making our approach a promising solution for real-world applications in which LR images are frequently encountered. The code and pretrained models will be publicly available on GitHub.

翻译：本研究提出一种特征知识蒸馏框架，通过利用从高分辨率图像中获取的知识来提升低分辨率人脸识别的性能。该框架通过减小高分辨率网络与低分辨率网络特征之间的距离，将高质量特征从高分辨率训练网络迁移至低分辨率训练网络。采用余弦相似度度量作为距离指标，有效对齐高分辨率与低分辨率特征。该方法区别于传统知识蒸馏框架中使用的L_p距离度量，在缩减不同分辨率特征间距离时具有更优的收敛特性。在不引入额外复杂模块的条件下，本框架在AgeDB-30基准测试上较先前最先进方法提升3%的识别率，同时保持对高分辨率图像的优异性能。通过统计分析验证了余弦相似度作为距离度量的有效性，使本方法成为频繁处理低分辨率图像的真实应用场景中的理想解决方案。相关代码与预训练模型将开源发布在GitHub平台。