# Modality Agnostic Heterogeneous Face Recognition with Switch Style Modulators

arXiv: https://arxiv.org/abs/2407.08640 · HTML: https://mlsanity.com/paper/2407.08640/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [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)
- [A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection](https://mlsanity.com/paper/2202.10286/) (2022)
- [Heterogeneous Face Recognition Using Domain Invariant Units](https://mlsanity.com/paper/2404.14343/) (2024)
- [Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation](https://mlsanity.com/paper/2307.07032/) (2023)
- [From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition](https://mlsanity.com/paper/2404.14247/) (2024)

## Built on by (most cited first, 4 of 4)

- [xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices](https://mlsanity.com/paper/2504.19646/) (2025)
- [The Invisible Threat: Evaluating the Vulnerability of Cross-Spectral Face Recognition to Presentation Attacks](https://mlsanity.com/paper/2505.00380/) (2025)
- [Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation](https://mlsanity.com/paper/2605.04769/) (2026)
- [ArtFace: Towards Historical Portrait Face Identification via Model Adaptation](https://mlsanity.com/paper/2508.20626/) (2025)

## Abstract (arXiv)

Heterogeneous Face Recognition (HFR) systems aim to enhance the capability of face recognition in challenging cross-modal authentication scenarios. However, the significant domain gap between the source and target modalities poses a considerable challenge for cross-domain matching. Existing literature primarily focuses on developing HFR approaches for specific pairs of face modalities, necessitating the explicit training of models for each source-target combination. In this work, we introduce a novel framework designed to train a modality-agnostic HFR method capable of handling multiple modalities during inference, all without explicit knowledge of the target modality labels. We achieve this by implementing a computationally efficient automatic routing mechanism called Switch Style Modulation Blocks (SSMB) that trains various domain expert modulators which transform the feature maps adaptively reducing the domain gap. Our proposed SSMB can be trained end-to-end and seamlessly integrated into pre-trained face recognition models, transforming them into modality-agnostic HFR models. We have performed extensive evaluations on HFR benchmark datasets to demonstrate its effectiveness. The source code and protocols will be made publicly available.