A Local Descriptor with Physiological Characteristic for Finger Vein Recognition

• URL: http://arxiv.org/abs/2004.07489v1
• Date: Thu, 16 Apr 2020 07:22:28 GMT
• Title: A Local Descriptor with Physiological Characteristic for Finger Vein Recognition
• Authors: Liping Zhang, Weijun Li, Xin Ning
• Abstract summary: We propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns. The proposed method outperforms most current state-of-the-art finger vein recognition methods.
• Score: 7.923285678279131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make feature has robustness against local changes in images, histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.

Related papers

• Progressive Retinal Image Registration via Global and Local Deformable Transformations [49.032894312826244]
  We propose a hybrid registration framework called HybridRetina. We use a keypoint detector and a deformation network called GAMorph to estimate the global transformation and local deformable transformation. Experiments on two widely-used datasets, FIRE and FLoRI21, show that our proposed HybridRetina significantly outperforms some state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-02T08:43:50Z)
• Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition [1.534667887016089]
  We propose a fusion based local matching approach towards latent fingerprint recognition. Proposed approach would integrate these handcrafted features with a recently proposed deep neural network embedding features in a multi-stage fusion approach.
  arXiv  Detail & Related papers  (2024-03-24T14:29:41Z)
• 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)
• Artificial Neural Networks for Finger Vein Recognition: A Survey [21.7602019087369]
  Finger vein recognition is highly stable and private. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, has superior performance. To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.
  arXiv  Detail & Related papers  (2022-08-29T02:29:07Z)
• Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trend [6.163420650113921]
  This review summarizes 46 papers about deep learning for finger vein image recognition from 2017 to 2021. Because veins are buried beneath the skin tissue, finger vein image recognition has an unparalleled advantage, which is not easily disturbed by external factors.
  arXiv  Detail & Related papers  (2022-07-05T16:14:36Z)
• An explainability framework for cortical surface-based deep learning [110.83289076967895]
  We develop a framework for cortical surface-based deep learning. First, we adapted a perturbation-based approach for use with surface data. We show that our explainability framework is not only able to identify important features and their spatial location but that it is also reliable and valid.
  arXiv  Detail & Related papers  (2022-03-15T23:16:49Z)
• From Noise to Feature: Exploiting Intensity Distribution as a Novel Soft Biometric Trait for Finger Vein Recognition [17.914339110401425]
  Most finger vein feature extraction algorithms achieve satisfactory performance due to their texture representation abilities. We exploit this kind of noise as a novel soft biometric trait for achieving better finger vein recognition performance.
  arXiv  Detail & Related papers  (2021-12-15T07:23:21Z)
• Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction [53.03469655641418]
  We present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. We establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases.
  arXiv  Detail & Related papers  (2021-05-18T20:47:45Z)
• Identifying the Origin of Finger Vein Samples Using Texture Descriptors [6.954694117813895]
  We use a texture classification approach to detect the origin of finger vein sample images. Based on eight publicly available finger vein datasets, we demonstrate excellent sensor model identification results.
  arXiv  Detail & Related papers  (2021-02-08T03:59:14Z)
• Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
  We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
  arXiv  Detail & Related papers  (2020-04-17T11:55:03Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

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.