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

• URL: http://arxiv.org/abs/2506.03635v1
• Date: Wed, 04 Jun 2025 07:27:33 GMT
• Title: FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition
• Abstract summary: We introduce FVeinSyn, a synthetic generator capable of producing diverse finger vein patterns with rich intra-class variations.<n>Using FVeinSyn, we created FingerVeinSyn-5M -- the largest available finger vein dataset.<n>Models pretrained on FingerVeinSyn-5M achieve an average 53.91% performance gain across multiple benchmarks.
• Score: 72.52509163913626
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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