Heterogeneous Face Recognition (HFR) focuses on matching faces from different
domains, for instance, thermal to visible images, making Face Recognition (FR)
systems more versatile for challenging scenarios. However, the domain gap
between these domains and the limited large-scale datasets in the target HFR
modalities make it challenging to develop robust HFR models from scratch. In
our work, we view different modalities as distinct styles and propose a method
to modulate feature maps of the target modality to address the domain gap. We
present a new Conditional Adaptive Instance Modulation (CAIM ) module that
seamlessly fits into existing FR networks, turning them into HFR-ready systems.
The CAIM block modulates intermediate feature maps, efficiently adapting to the
style of the source modality and bridging the domain gap. Our method enables
end-to-end training using a small set of paired samples. We extensively
evaluate the proposed approach on various challenging HFR benchmarks, showing
that it outperforms state-of-the-art methods. The source code and protocols for
reproducing the findings will be made publicly available

我们的研究工作着眼于异构人脸识别，提出了一种新的条件自适应实例调制模块 (CAIM)，可以有效地适应不同的源模态，并弥合领域差距，实现全面训练。通过在多个挑战性的基准测试中进行广泛评估，证明我们的方法优于现有的方法。