构建了500万张指纹静脉图像的大规模数据集，推动深度学习在该领域应用
FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition
- 用合成生成器FVeinSyn创建多样化静脉图案，模拟多种真实干扰
- 数据集含5万个体、每指100种变化，实现53.91%性能提升
- 首个全标注静脉数据集，适合做预训练或小样本微调的研究者
指纹静脉识别面临大规模公开数据集匮乏的挑战。现有数据集身份数量少、每指样本有限，制约深度学习方法发展。为此，我们提出FVeinSyn合成生成器，可生成具有丰富类内差异的多样指纹静脉图案。基于该生成器，我们构建了目前最大规模的指纹静脉数据集FingerVeinSyn-5M，包含50,000个唯一手指的500万张图像，每个手指包含100种变化，涵盖平移、旋转、缩放、滚动、不同曝光度、皮肤散射模糊、光学模糊和运动模糊等。FingerVeinSyn-5M是首个提供完整标注的指纹静脉图像数据集，支持深度学习应用。在该数据集上预训练并用少量真实数据微调的模型，在多个基准测试中平均性能提升53.91%。数据集已开源：https://github.com/EvanWang98/FingerVeinSyn-5M。
原文摘要 · Abstract (English)
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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