# Dual Variational Generation for Low-Shot Heterogeneous Face Recognition

arXiv: https://arxiv.org/abs/1903.10203 · HTML: https://mlsanity.com/paper/1903.10203/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 25 of 25)

- [SOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfer](https://mlsanity.com/paper/2104.10567/) (2021)
- [Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning](https://mlsanity.com/paper/2112.00568/) (2021)
- [T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2209.08814/) (2022)
- [Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles](https://mlsanity.com/paper/2210.06529/) (2022)
- [MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2303.14626/) (2023)
- [Everything's Talkin': Pareidolia Face Reenactment](https://mlsanity.com/paper/2104.03061/) (2021)
- [RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition](https://mlsanity.com/paper/2307.14016/) (2023)
- [Contrastive Attention Network with Dense Field Estimation for Face Completion](https://mlsanity.com/paper/2112.10310/) (2021)
- [Beyond the Visible: A Survey on Cross-spectral Face Recognition](https://mlsanity.com/paper/2201.04435/) (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)
- [xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices](https://mlsanity.com/paper/2504.19646/) (2025)
- [Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement](https://mlsanity.com/paper/2206.04854/) (2022)
- [Modality Agnostic Heterogeneous Face Recognition with Switch Style Modulators](https://mlsanity.com/paper/2407.08640/) (2024)
- [Physically-Based Face Rendering for NIR-VIS Face Recognition](https://mlsanity.com/paper/2211.06408/) (2022)
- [From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition](https://mlsanity.com/paper/2404.14247/) (2024)
- [Diff-Palm: Realistic Palmprint Generation with Polynomial Creases and Intra-Class Variation Controllable Diffusion Models](https://mlsanity.com/paper/2503.18312/) (2025)
- [Multimodal Face Synthesis from Visual Attributes](https://mlsanity.com/paper/2104.04362/) (2021)
- [Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition](https://mlsanity.com/paper/2204.11434/) (2022)
- [Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2211.09350/) (2022)
- [Rethinking the Domain Gap in Near-infrared Face Recognition](https://mlsanity.com/paper/2312.00627/) (2023)
- [Lightweight Cross-Spectral Face Recognition via Contrastive Alignment and Distillation](https://mlsanity.com/paper/2605.04769/) (2026)
- [Styleverse: Towards Identity Stylization across Heterogeneous Domains](https://mlsanity.com/paper/2203.00861/) (2022)
- [Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination](https://mlsanity.com/paper/2203.16669/) (2022)
- [Human-Imperceptible Physical Adversarial Attack for NIR Face Recognition Models](https://mlsanity.com/paper/2504.15823/) (2025)

## Abstract (arXiv)

Heterogeneous Face Recognition (HFR) is a challenging issue because of the large domain discrepancy and a lack of heterogeneous data. This paper considers HFR as a dual generation problem, and proposes a novel Dual Variational Generation (DVG) framework. It generates large-scale new paired heterogeneous images with the same identity from noise, for the sake of reducing the domain gap of HFR. Specifically, we first introduce a dual variational autoencoder to represent a joint distribution of paired heterogeneous images. Then, in order to ensure the identity consistency of the generated paired heterogeneous images, we impose a distribution alignment in the latent space and a pairwise identity preserving in the image space. Moreover, the HFR network reduces the domain discrepancy by constraining the pairwise feature distances between the generated paired heterogeneous images. Extensive experiments on four HFR databases show that our method can significantly improve state-of-the-art results. The related code is available at https://github.com/BradyFU/DVG.