xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices
This work addresses the deployment challenge of heterogeneous face recognition on resource-constrained edge devices, offering an incremental improvement over existing methods.
The paper tackles the problem of efficient cross-spectral face recognition for edge devices by proposing a lightweight hybrid CNN-Transformer framework, achieving state-of-the-art performance on multiple benchmarks with low computational overhead.
Heterogeneous Face Recognition (HFR) addresses the challenge of matching face images across different sensing modalities, such as thermal to visible or near-infrared to visible, expanding the applicability of face recognition systems in real-world, unconstrained environments. While recent HFR methods have shown promising results, many rely on computation-intensive architectures, limiting their practicality for deployment on resource-constrained edge devices. In this work, we present a lightweight yet effective HFR framework by adapting a hybrid CNN-Transformer architecture originally designed for face recognition. Our approach enables efficient end-to-end training with minimal paired heterogeneous data while preserving strong performance on standard RGB face recognition tasks. This makes it a compelling solution for both homogeneous and heterogeneous scenarios. Extensive experiments across multiple challenging HFR and face recognition benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches while maintaining a low computational overhead.