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

TL;DR

This paper introduces FingerVeinSyn-5M, the largest synthetic finger vein dataset with 5 million samples, enabling significant improvements in deep learning-based finger vein recognition through extensive data augmentation and annotation.

Contribution

We developed FVeinSyn, a synthetic generator for diverse finger vein patterns, and created FingerVeinSyn-5M, the largest annotated dataset to advance deep learning methods in finger vein recognition.

Findings

Pretraining on FingerVeinSyn-5M improves recognition accuracy.

Synthetic data enhances model robustness to variations.

Minimal real data fine-tuning yields significant performance gains.

Abstract

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.