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.

本文旨在解决异构人脸识别中的领域差异以及在目标领域中大规模数据集的限制问题，在这篇论文中，我们提出了一种新的框架，将不同光谱的人脸识别模态视为不同的风格，通过条件自适应实例调制（CAIM）模块适应中间特征图的风格，成功地在异构人脸识别上实现了端到端训练，并在多个数据集上展示了优越的性能，其中训练所需的成对样本数量最少。