轻量级跨模态人脸识别，高效适配边缘设备
Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation
- 用混合CNN-Transformer结构，仅需少量配对数据即可端到端训练
- 在多个跨模态识别数据集上达到顶尖性能，计算开销低
- 兼顾同模态与异模态场景，适合资源受限的部署环境
异模态人脸识别（HFR）旨在匹配不同传感模态下获取的人脸图像，如热成像转可见光或近红外转可见光，提升复杂环境下人脸识别系统的可用性。尽管近期方法在性能上取得显著进展，但多数依赖高计算成本的模型，难以在资源受限的边缘设备上部署。本文提出一种轻量级且高效的HFR框架，基于原本用于RGB同模态人脸识别的混合CNN-Transformer模型进行适配。该方法仅需少量配对异模态数据即可实现高效端到端训练，同时在标准RGB人脸识别基准上保持强性能，适用于同模态与异模态双重场景。在多个挑战性HFR及人脸识别基准上的综合实验表明，本方法在保持低计算需求的同时，达到了当前最优或具有竞争力的性能。
原文摘要 · Abstract (English)
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.
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