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.
Abstract (translated)
异构人脸识别（Heterogeneous Face Recognition, HFR）旨在解决跨不同传感模式匹配面部图像的挑战，如从热成像到可见光或近红外到可见光的情况，从而扩展了面部识别系统在现实世界中无约束环境中的适用性。尽管最近的一些HFR方法展示了令人鼓舞的结果，但许多这些方法依赖于计算密集型架构，这限制了它们在资源受限的边缘设备上的实用性。在这项工作中，我们提出了一种轻量级且有效的HFR框架，通过适应一种最初为面部识别设计的混合CNN-Transformer架构来实现这一目标。我们的方法能够在使用少量配对异构数据的情况下进行高效的端到端训练，并保持强大的标准RGB面部识别任务性能。这使其成为解决同质和异质场景问题的一个引人注目的解决方案。在多个具有挑战性的HFR和面部识别基准测试上的广泛实验表明，我们的方法始终优于现有最佳方法，同时保持较低的计算开销。
URL
https://arxiv.org/abs/2504.19646
