xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices

• URL: http://arxiv.org/abs/2504.19646v1
• Date: Mon, 28 Apr 2025 10:03:11 GMT
• Title: xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices
• Authors: Anjith George, Sebastien Marcel,
• Abstract summary: Heterogeneous Face Recognition (HFR) addresses the challenge of matching face images across different sensing modalities.<n>We present a lightweight yet effective HFR framework by adapting a hybrid CNN-Transformer architecture.<n>Our approach enables efficient end-to-end training with minimal paired heterogeneous data while preserving strong performance on standard RGB face recognition tasks.
• Score: 4.910937238451485
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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