# FVeinSyn: Synthetic Finger Vein Image Generator

arXiv: https://arxiv.org/abs/2608.27527 · HTML: https://mlsanity.com/paper/2608.27527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 87 references that are in the map.
- First posted on arXiv 27 Aug 2026.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [The Devil of Face Recognition is in the Noise](https://mlsanity.com/paper/1807.11649/) (2018)
- [Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?](https://mlsanity.com/paper/1912.07398/) (2019)
- [HaGRID - HAnd Gesture Recognition Image Dataset](https://mlsanity.com/paper/2206.08219/) (2022)
- [UMDFaces: An Annotated Face Dataset for Training Deep Networks](https://mlsanity.com/paper/1611.01484/) (2016)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [PrintsGAN: Synthetic Fingerprint Generator](https://mlsanity.com/paper/2201.03674/) (2022)
- [Face Generation and Editing with StyleGAN: A Survey](https://mlsanity.com/paper/2212.09102/) (2022)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions](https://mlsanity.com/paper/2404.13791/) (2024)
- [RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition](https://mlsanity.com/paper/2307.14016/) (2023)
- [T-FAKE: Synthesizing Thermal Images for Facial Landmarking](https://mlsanity.com/paper/2408.15127/) (2024)
- [Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement](https://mlsanity.com/paper/2206.04854/) (2022)
- [Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms](https://mlsanity.com/paper/2406.06932/) (2024)
- [CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition](https://mlsanity.com/paper/2409.14774/) (2024)
- [Diff-Palm: Realistic Palmprint Generation with Polynomial Creases and Intra-Class Variation Controllable Diffusion Models](https://mlsanity.com/paper/2503.18312/) (2025)
- [DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategy](https://mlsanity.com/paper/2501.06533/) (2025)
- [PVTree: Realistic and Controllable Palm Vein Generation for Recognition Tasks](https://mlsanity.com/paper/2503.02547/) (2025)
- [ColorVein: Colorful Cancelable Vein Biometrics](https://mlsanity.com/paper/2504.14253/) (2025)
- [FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition](https://mlsanity.com/paper/2506.03635/) (2025)

## Abstract (arXiv)

A major challenge in finger vein recognition is the lack of large-scale public datasets. Existing datasets contain few identities and limited samples per finger, restricting the advancement of deep learning-based methods. To address this, we propose FVeinSyn, a large-scale controllable synthetic data generation framework for finger vein. It explicitly decouples synthesis of vascular topology and imaging appearance to mitigate the limitations caused by insufficient training samples, such as inadequate identity diversity and restricted realism. Specifically: first, a finger vein identity generator models vascular topology under physiological and geometric constraints using stochastic L-systems, producing anatomically valid and identity-distinctive vascular patterns. Then, a cascaded region-aware GAN renders the topological maps into realistic near-infrared images. Finally, an intra-class diversity generator introduces geometric and optical perturbations to simulate realistic intra-class variations. Using FVeinSyn, we generated 500,000 images (10,000 vein identities, 50 samples per identity) and conducted extensive evaluations. Results show that FVeinSyn holds significant advantages in realism, identity diversity, vascular pattern consistency, and intra-class diversity. Models trained with FVeinSyn outperform real-data-only baselines a cross eight public datasets, achieving an average accuracy improvement of 27.43%. The code is available at: https://github.com/EvanWang98/Synthetic-Finger-Vein-Generator.