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Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation


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 introduced 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.


George, A. Marcel, S. (2023). Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation. IJCB.


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.


  • Face Recognition

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Layer 1 to Layer N represent the frozen blocks of layers from a pretrained Face Recognition (FR) model. Architecture of the Conditional Adaptive Instance Modulation (CAIM) block is shown on the right. The CAIM module is inserted between the initial blocks.

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