Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

• URL: http://arxiv.org/abs/2303.08665v2
• Date: Tue, 5 Sep 2023 12:35:36 GMT
• Title: Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation
• Authors: Yuhang Lu, Touradj Ebrahimi
• Abstract summary: Cross-resolution face recognition is a challenging problem for modern deep face recognition systems. 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.
• Score: 12.090322373964124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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