FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912412888727552 |
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| author | Wang, Yinfan Gui, Jie Yu, Baosheng Li, Qi Sun, Zhenan Kannala, Juho Zhao, Guoying |
| author_facet | Wang, Yinfan Gui, Jie Yu, Baosheng Li, Qi Sun, Zhenan Kannala, Juho Zhao, Guoying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition Wang, Yinfan Gui, Jie Yu, Baosheng Li, Qi Sun, Zhenan Kannala, Juho Zhao, Guoying Computer Vision and Pattern Recognition 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. |
| title | FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.03635 |