CONF George_IJCB2023_2023/IDIAP Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation George, Anjith Marcel, S├ębastien EXTERNAL PUBLIC IJCB 2023 Heterogeneous Face Recognition (HFR) aims to match face images acrossdifferent domains, such as thermal and visible spectra, expanding theapplicability of Face Recognition (FR) systems to challenging scenarios.However, the domain gap and limited availability of large-scale datasets in thetarget domain make training robust and invariant HFR models from scratchdifficult. In this work, we treat different modalities as distinct styles andpropose a framework to adapt feature maps, bridging the domain gap. Weintroduce a novel Conditional Adaptive Instance Modulation (CAIM) module thatcan be integrated into pre-trained FR networks, transforming them into HFRnetworks. The CAIM block modulates intermediate feature maps, to adapt thestyle of the target modality effectively bridging the domain gap. Our proposedmethod 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. Thesource code and protocols for reproducing the findings will be made publiclyavailable.