  Cross-resolution face recognition has become a challenging problem for modern
deep face recognition systems. It aims at matching a low-resolution probe image
with high-resolution gallery images registered in a database. Existing methods
mainly leverage prior information from high-resolution images by either
reconstructing facial details with super-resolution techniques or learning a
unified feature space. To address this challenge, this paper proposes a new
approach that enforces the network to focus on the discriminative information
stored in the low-frequency components of a low-resolution image. A
cross-resolution knowledge distillation paradigm is first employed as the
learning framework. Then, an identity-preserving network, WaveResNet, and a
wavelet similarity loss are designed to capture low-frequency details and boost
performance. Finally, an image degradation model is conceived to simulate more
realistic low-resolution 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.
