轻量级跨模态人脸识别框架，适配边缘设备高效运行
xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices
- 融合CNN与Transformer的轻量架构，支持端到端训练
- 仅需少量配对数据即可实现高性能跨模态识别
- 兼顾边缘部署与主流可见光识别任务表现
异构人脸识别（HFR）解决热成像到可见光或近红外到可见光等不同传感模态间的人脸匹配问题，拓展了人脸识别在真实、非受限环境中的应用。尽管近期方法已取得良好效果，但多数依赖计算密集型架构，难以部署于资源受限的边缘设备。本文提出一种轻量高效的HFR框架，基于原为可见光人脸识别设计的混合CNN-Transformer架构进行改进。该方法可在极少量配对异构数据下实现高效端到端训练，同时保持在标准可见光人脸识别任务上的强性能，适用于同质与异质场景。在多个具有挑战性的HFR及人脸识别基准上的广泛实验表明，本方法持续优于当前最优方案，且计算开销低。
原文摘要 · Abstract (English)
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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