Heterogeneous Face Recognition (HFR) aims to expand the applicability of Face Recognition (FR) systems to challenging scenarios, enabling the matching of face images across different domains, such as matching thermal images to visible spectra. However, the development of HFR systems is challenging because of the significant domain gap between modalities and the lack of availability of large-scale paired multi-channel data. In this work, we leverage a pretrained face recognition model as a teacher network to learn domaininvariant network layers called Domain-Invariant Units (DIU) to reduce the domain gap. The proposed DIU can be trained effectively even with a limited amount of paired training data, in a contrastive distillation framework. This proposed approach has the potential to enhance pretrained models, making them more adaptable to a wider range of variations in data. We extensively evaluate our approach on multiple challenging benchmarks, demonstrating superior performance compared to state-of-the-art methods.

翻译：异质人脸识别旨在将人脸识别系统的适用性扩展至具有挑战性的场景，实现跨域人脸图像的匹配，例如将热成像图像与可见光谱图像进行匹配。然而，由于模态间存在显著的域差异，且缺乏大规模配对多通道数据，异质人脸识别系统的开发面临巨大挑战。本研究利用预训练的人脸识别模型作为教师网络，学习名为"域不变单元"的域不变网络层以减少域差异。即使仅使用有限数量的配对训练数据，所提出的域不变单元也能在对比蒸馏框架下有效训练。该方法能增强预训练模型，使其对更广泛的数据变化具有更强的适应性。我们在多个具有挑战性的基准数据集上进行了充分评估，结果表明该方法相比现有最优方法具有更优性能。