# Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation

arXiv: https://arxiv.org/abs/2307.07032 · HTML: https://mlsanity.com/paper/2307.07032/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis](https://mlsanity.com/paper/1701.02096/) (2017)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition](https://mlsanity.com/paper/1708.02412/) (2017)
- [DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition](https://mlsanity.com/paper/2009.09399/) (2020)
- [Dual Variational Generation for Low-Shot Heterogeneous Face Recognition](https://mlsanity.com/paper/1903.10203/) (2019)
- [Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles](https://mlsanity.com/paper/2210.06529/) (2022)
- [Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces](https://mlsanity.com/paper/1708.02681/) (2017)
- [A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection](https://mlsanity.com/paper/2202.10286/) (2022)

## Built on by (most cited first, 5 of 5)

- [Heterogeneous Face Recognition Using Domain Invariant Units](https://mlsanity.com/paper/2404.14343/) (2024)
- [xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices](https://mlsanity.com/paper/2504.19646/) (2025)
- [Modality Agnostic Heterogeneous Face Recognition with Switch Style Modulators](https://mlsanity.com/paper/2407.08640/) (2024)
- [From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition](https://mlsanity.com/paper/2404.14247/) (2024)
- [Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation](https://mlsanity.com/paper/2605.04769/) (2026)

## Abstract (arXiv)

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.