  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.
