Bridging the Gap: Heterogeneous Face Recognition with Conditional
  Adaptive Instance Modulation
Anjith George Sebastien Marcel
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.