# Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles

arXiv: https://arxiv.org/abs/2210.06529 · HTML: https://mlsanity.com/paper/2210.06529/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 81 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 25 of 25)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [Towards Fast, Accurate and Stable 3D Dense Face Alignment](https://mlsanity.com/paper/2009.09960/) (2020)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [FaceX-Zoo: A PyTorch Toolbox for Face Recognition](https://mlsanity.com/paper/2101.04407/) (2021)
- [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)
- [Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks](https://mlsanity.com/paper/2007.11469/) (2020)
- [Dual Variational Generation for Low-Shot Heterogeneous Face Recognition](https://mlsanity.com/paper/1903.10203/) (2019)
- [A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset](https://mlsanity.com/paper/2101.02637/) (2021)
- [Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral Hallucination and Low-rank Embedding](https://mlsanity.com/paper/1611.06638/) (2016)
- [Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis](https://mlsanity.com/paper/1904.07344/) (2019)
- [Cross-Domain Identification for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2008.08473/) (2020)
- [Disentangled Variational Representation for Heterogeneous Face Recognition](https://mlsanity.com/paper/1809.01936/) (2018)
- [TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition](https://mlsanity.com/paper/1712.02514/) (2017)
- [Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces](https://mlsanity.com/paper/1708.02681/) (2017)
- [The High-Quality Wide Multi-Channel Attack (HQ-WMCA) database](https://mlsanity.com/paper/2009.09703/) (2020)

## Built on by (most cited first, 17 of 17)

- [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)
- [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)
- [Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance](https://mlsanity.com/paper/2504.18886/) (2025)
- [CATFace: Cross-Attribute-Guided Transformer with Self-Attention Distillation for Low-Quality Face Recognition](https://mlsanity.com/paper/2401.03037/) (2024)
- [From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition](https://mlsanity.com/paper/2404.14247/) (2024)
- [DiffX: Guide Your Layout to Cross-Modal Generative Modeling](https://mlsanity.com/paper/2407.15488/) (2024)
- [Evaluating Multimodal Large Language Models for Heterogeneous Face Recognition](https://mlsanity.com/paper/2601.15406/) (2026)
- [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)
- [MTVDiff: Multimodal Conditional Latent Diffusion for Enhanced Thermal-to-Visible Face Translation](https://mlsanity.com/paper/2607.19886/) (2026)
- [Cross-Spectral Attention for Unsupervised RGB-IR Face Verification and Person Re-identification](https://mlsanity.com/paper/2411.19215/) (2024)
- [DriveFace: A Cross-Spectral Through-Glass Face Dataset for On-the-Move Vehicular Border Control](https://mlsanity.com/paper/2607.13515/) (2026)
- [Improving Low-Resolution Face Recognition under Limited Data: How Synthetic Data Generation Can Close the Domain Gap](https://mlsanity.com/paper/2608.06580/) (2026)
- [SynThermFace: Amplifying Limited Paired Data for Visible-Thermal Face Recognition via Synthetic Data Generation](https://mlsanity.com/paper/2609.10303/) (2026)
- [Frequency Routing and Identity Conditioning for Thermal-to-Visible Face Translation](https://mlsanity.com/paper/2610.17859/) (2026)

## Abstract (arXiv)

Heterogeneous Face Recognition (HFR) refers to matching face images captured in different domains, such as thermal to visible images (VIS), sketches to visible images, near-infrared to visible, and so on. This is particularly useful in matching visible spectrum images to images captured from other modalities. Though highly useful, HFR is challenging because of the domain gap between the source and target domain. Often, large-scale paired heterogeneous face image datasets are absent, preventing training models specifically for the heterogeneous task. In this work, we propose a surprisingly simple, yet, very effective method for matching face images across different sensing modalities. The core idea of the proposed approach is to add a novel neural network block called Prepended Domain Transformer (PDT) in front of a pre-trained face recognition (FR) model to address the domain gap. Retraining this new block with few paired samples in a contrastive learning setup was enough to achieve state-of-the-art performance in many HFR benchmarks. The PDT blocks can be retrained for several source-target combinations using the proposed general framework. The proposed approach is architecture agnostic, meaning they can be added to any pre-trained FR models. Further, the approach is modular and the new block can be trained with a minimal set of paired samples, making it much easier for practical deployment. The source code and protocols will be made available publicly.