Heterogeneous Face Recognition (HFR) aims to match face images across different domains, such as thermal and visible spectra, expanding the applicability of Face Recognition (FR) systems to challenging scenarios. However, the domain gap and limited availability of large-scale datasets in the target domain make training robust and invariant HFR models from scratch difficult. In this work, we treat different modalities as distinct styles and propose a framework to adapt feature maps, bridging the domain gap. We introduce a novel Conditional Adaptive Instance Modulation (CAIM) module that can be integrated into pre-trained FR networks, transforming them into HFR networks. The CAIM block modulates intermediate feature maps, to adapt the style of the target modality effectively bridging the domain gap. Our proposed method allows for end-to-end training with a minimal number of paired samples. We extensively evaluate our approach on multiple challenging benchmarks, demonstrating superior performance compared to 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）旨在匹配跨不同域（如热红外与可见光谱）的人脸图像，从而将人脸识别（FR）系统的适用性扩展至具有挑战性的场景。然而，域差距以及目标域中大规模数据集的有限可用性，使得从头开始训练鲁棒且不变性的HFR模型变得困难。在本工作中，我们将不同模态视为不同风格，并提出一个框架来调整特征图，从而弥合域差距。我们引入了一种新颖的条件自适应实例调制（CAIM）模块，该模块可集成到预训练的FR网络中，将其转化为HFR网络。CAIM模块通过调制中间特征图，有效适配目标模态的风格，从而弥合域差距。所提方法允许使用最少数量的配对样本进行端到端训练。我们在多个具有挑战性的基准上进行了广泛评估，结果表明该方法相比现有最先进方法具有优越性能。用于复现研究结果的源代码和协议将公开发布。