# FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition

arXiv: https://arxiv.org/abs/2506.03635 · HTML: https://mlsanity.com/paper/2506.03635/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [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)
- [HaGRID - HAnd Gesture Recognition Image Dataset](https://mlsanity.com/paper/2206.08219/) (2022)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review](https://mlsanity.com/paper/2205.10179/) (2022)
- [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)

## Built on by (most cited first, 1 of 1)

- [FVeinSyn: Synthetic Finger Vein Image Generator](https://mlsanity.com/paper/2608.27527/) (2026)

## 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 introduce FVeinSyn, a synthetic generator capable of producing diverse finger vein patterns with rich intra-class variations. Using FVeinSyn, we created FingerVeinSyn-5M -- the largest available finger vein dataset -- containing 5 million samples from 50,000 unique fingers, each with 100 variations including shift, rotation, scale, roll, varying exposure levels, skin scattering blur, optical blur, and motion blur. FingerVeinSyn-5M is also the first to offer fully annotated finger vein images, supporting deep learning applications in this field. Models pretrained on FingerVeinSyn-5M and fine-tuned with minimal real data achieve an average 53.91% performance gain across multiple benchmarks. The dataset is publicly available at: https://github.com/EvanWang98/FingerVeinSyn-5M.