Low-resolution face recognition (LRFR) has become a challenging problem for modern deep face recognition systems. Existing methods mainly leverage prior information from high-resolution (HR) images by either reconstructing facial details with super-resolution techniques or learning a unified feature space. To address this issue, this paper proposes a novel approach which enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution (LR) image. A cross-resolution knowledge distillation paradigm is first employed as the learning framework. An identity-preserving network, WaveResNet, and a wavelet similarity loss are then designed to capture low-frequency details and boost performance. Finally, an image degradation model is conceived to simulate more realistic LR training data. Consequently, extensive experimental results show that the proposed method consistently outperforms the baseline model and other state-of-the-art methods across a variety of image resolutions.

翻译：低分辨率人脸识别（LRFR）已成为现代深度人脸识别系统中的一个挑战性问题。现有方法主要利用高分辨率（HR）图像的先验信息，通过超分辨率技术重建面部细节或学习统一特征空间。为解决这一问题，本文提出了一种新方法，迫使网络聚焦于低分辨率（LR）图像低频分量中存储的判别性信息。首先采用跨分辨率知识蒸馏范式作为学习框架；随后设计了身份保持网络WaveResNet和小波相似度损失，以捕获低频细节并提升性能；最后构思了图像退化模型以模拟更真实的LR训练数据。大量实验结果表明，在各种图像分辨率下，所提方法始终优于基线模型及其他最先进方法。