Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation
It addresses the need for efficient heterogeneous face recognition on resource-limited edge devices, offering a practical solution for real-world deployment.
The paper introduces a lightweight CNN-Transformer framework for cross-spectral face recognition that achieves state-of-the-art or competitive performance on heterogeneous face recognition benchmarks while requiring minimal paired training data and low computational cost.
Heterogeneous Face Recognition (HFR) aims at matching face images captured across different sensing modalities, such as thermal-to-visible or near-infrared-to-visible, enhancing the usability of face recognition systems in challenging real-world conditions. Although recent HFR methods have achieved significant improvements in performance, many rely on computationally expensive models, making them impractical for deployment on resource-limited edge devices. In this work, we introduce a lightweight yet effective HFR framework by adapting a hybrid CNN-Transformer model originally developed for RGB homogeneous face recognition. Our approach enables efficient end-to-end training with only a small amount of paired heterogeneous data, while still maintaining strong performance on standard RGB face recognition benchmarks. This makes it suitable for both homogeneous and heterogeneous settings. Comprehensive experiments on several challenging HFR and face recognition benchmarks show that our method achieves state-of-the-art or competitive performance while keeping computational requirements low.