Heterogeneous Face Recognition (HFR) focuses on matching faces from different domains, for instance, thermal to visible images, making Face Recognition (FR) systems more versatile for challenging scenarios. However, the domain gap between these domains and the limited large-scale datasets in the target HFR modalities make it challenging to develop robust HFR models from scratch. In our work, we view different modalities as distinct styles and propose a method to modulate feature maps of the target modality to address the domain gap. We present a new Conditional Adaptive Instance Modulation (CAIM ) module that seamlessly fits into existing FR networks, turning them into HFR-ready systems. The CAIM block modulates intermediate feature maps, efficiently adapting to the style of the source modality and bridging the domain gap. Our method enables end-to-end training using a small set of paired samples. We extensively evaluate the proposed approach on various challenging HFR benchmarks, showing that it outperforms state-of-the-art methods. The source code and protocols for reproducing the findings will be made publicly available

翻译：异质人脸识别（HFR）致力于匹配来自不同领域的人脸，例如从热成像图像到可见光图像，使人脸识别（FR）系统在具有挑战性的场景中更具通用性。然而，这些领域之间的域差异以及目标HFR模态中大规模数据集的缺乏，使得从头开发鲁棒的HFR模型变得困难。在本文中，我们将不同模态视为不同风格，并提出一种方法来调制目标模态的特征图以解决域差异。我们提出了一种新的条件自适应实例调制（CAIM）模块，该模块能够无缝集成到现有的FR网络中，将其转化为支持HFR的系统。CAIM模块调制中间特征图，高效地适应源模态风格并弥合域差异。我们的方法能利用少量配对样本进行端到端训练。我们在多个具有挑战性的HFR基准上对该方法进行了广泛评估，结果表明其优于现有最先进方法。用于复现实验结果的源代码和协议将公开发布。