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
• Authors: Yinfan Wang, Jie Gui, Baosheng Yu, Qi Li, Zhenan Sun, Juho Kannala, Guoying Zhao,
• 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.

Related papers

• Motion Transfer-Driven intra-class data augmentation for Finger Vein Recognition [14.95018662462898]
  Finger vein recognition (FVR) has emerged as a secure biometric technique because of the confidentiality of vascular bio-information.<n>We propose a novel motion transfer model for finger vein image data augmentation via modeling the actual finger posture and rotational movements.<n> Experiments conducted on three public finger vein databases demonstrate that the proposed motion transfer model can effectively improve recognition accuracy.
  arXiv  Detail & Related papers  (2024-12-29T02:51:57Z)
• $\textit{sweet}$- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments [19.054919189900303]
  We present a contactless vascular biometrics sensor platform named sweet. It can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS)
  arXiv  Detail & Related papers  (2024-04-14T23:17:01Z)
• Fingervein Verification using Convolutional Multi-Head Attention Network [3.700129710233692]
  We introduce a novel fingervein verification technique using a convolutional multihead attention network called VeinAtnNet. The proposed VeinAtnNet is designed to achieve light weight with a smaller number of learnable parameters while extracting discriminant information from both normal and enhanced fingervein images.
  arXiv  Detail & Related papers  (2023-10-25T17:38:16Z)
• Advancing 3D finger knuckle recognition via deep feature learning [51.871256510747465]
  Contactless 3D finger knuckle patterns have emerged as an effective biometric identifier due to its discriminativeness, visibility from a distance, and convenience. Recent research has developed a deep feature collaboration network which simultaneously incorporates intermediate features from deep neural networks with multiple scales. This paper advances this approach by investigating the possibility of learning a discriminative feature vector with the least possible dimension for representing 3D finger knuckle images.
  arXiv  Detail & Related papers  (2023-01-07T20:55:16Z)
• AFR-Net: Attention-Driven Fingerprint Recognition Network [47.87570819350573]
  We improve initial studies on the use of vision transformers (ViT) for biometric recognition, including fingerprint recognition. We propose a realignment strategy using local embeddings extracted from intermediate feature maps within the networks to refine the global embeddings in low certainty situations. This strategy can be applied as a wrapper to any existing deep learning network (including attention-based, CNN-based, or both) to boost its performance.
  arXiv  Detail & Related papers  (2022-11-25T05:10:39Z)
• Finger Multimodal Feature Fusion and Recognition Based on Channel Spatial Attention [8.741051302995755]
  We propose a multimodal biometric fusion recognition algorithm based on fingerprints and finger veins. For each pair of fingerprint and finger vein images, we first propose a simple and effective Convolutional Neural Network (CNN) to extract features. Then, we build a multimodal feature fusion module (Channel Spatial Attention Fusion Module, CSAFM) to fully fuse the complementary information between fingerprints and finger veins.
  arXiv  Detail & Related papers  (2022-09-06T10:48:30Z)
• BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection [63.447493500066045]
  This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects.
  arXiv  Detail & Related papers  (2022-07-27T09:26:15Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps [46.316638161863025]
  We present Multi-FinGAN, a fast generative multi-finger grasp sampling method that synthesizes high quality grasps directly from RGB-D images in about a second. We experimentally validate and benchmark our method against a standard grasp-sampling method on 790 grasps in simulation and 20 grasps on a real Franka Emika Panda. Remarkably, our approach is up to 20-30 times faster than the baseline, a significant improvement that opens the door to feedback-based grasp re-planning and task informative grasping.
  arXiv  Detail & Related papers  (2020-12-17T16:08:18Z)
• IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition [11.917058689674327]
  We introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. This dataset contains more than 4,000 gesture samples and 800,000 RGB frames from 50 distinct subjects. With our dataset, the performance of three 3D-CNN models is evaluated on the tasks of isolated and continuous real-time HGR.
  arXiv  Detail & Related papers  (2020-04-20T08:52:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.