  Heterogeneous Face Recognition (HFR) systems aim to enhance the capability of
face recognition in challenging cross-modal authentication scenarios. However,
the significant domain gap between the source and target modalities poses a
considerable challenge for cross-domain matching. Existing literature primarily
focuses on developing HFR approaches for specific pairs of face modalities,
necessitating the explicit training of models for each source-target
combination. In this work, we introduce a novel framework designed to train a
modality-agnostic HFR method capable of handling multiple modalities during
inference, all without explicit knowledge of the target modality labels. We
achieve this by implementing a computationally efficient automatic routing
mechanism called Switch Style Modulation Blocks (SSMB) that trains various
domain expert modulators which transform the feature maps adaptively reducing
the domain gap. Our proposed SSMB can be trained end-to-end and seamlessly
integrated into pre-trained face recognition models, transforming them into
modality-agnostic HFR models. We have performed extensive evaluations on HFR
benchmark datasets to demonstrate its effectiveness. The source code and
protocols will be made publicly available.
