scholarly journals Finger Vein Recognition Using Optimal Partitioning Uniform Rotation Invariant LBP Descriptor

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Bang Chao Liu ◽  
Shan Juan Xie ◽  
Dong Sun Park
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Recognition Rate ◽  
Vein Pattern ◽  
Low Contrast ◽  
Finger Vein ◽  
Long Time ◽  
Vein Recognition ◽  
Lbp Descriptor ◽  
Selection Algorithms

As a promising biometric system, finger vein identification has been studied widely and many relevant researches have been proposed. However, it is hard to extract a satisfied finger vein pattern due to the various vein thickness, illumination, low contrast region, and noise existing. And most of the feature extraction algorithms rely on high-quality finger vein database and take a long time for a large dimensional feature vector. In this paper, we proposed two block selection methods which are based on the estimate of the amount of information in each block and the contribution of block location by looking at recognition rate of each block position to reduce feature extraction time and matching time. The specific approach is to find out some local finger vein areas with low-quality and noise, which will be useless for feature description. Local binary pattern (LBP) descriptors are proposed to extract the finger vein pattern feature. Two finger vein databases are taken to test our algorithm performance. Experimental results show that proposed block selection algorithms can reduce the feature vector dimensionality in a large extent.

scholarly journals A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1885
Author(s):  
Qiong Yao ◽  
Dan Song ◽  
Xiang Xu ◽  
Kun Zou
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Spatial Information ◽  
Individual Recognition ◽  
Vein Pattern ◽  
Low Contrast ◽  
Feature Extraction Method ◽  
Structure Information ◽  
Finger Vein ◽  
Specific Implementation

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

scholarly journals Double stages of feature extarction-based GFPMI for colored finger vein identification

2020 ◽  
Vol 18 (2) ◽  
pp. 927
Author(s):  
Dawlat Mustafa Sulaiman ◽  
Adnan Mohsin Abdulazeez ◽  
Habibollah Haron
Keyword(s):  
Feature Extraction ◽  
Texture Features ◽  
Biometric Identification ◽  
Gray Level ◽  
Image Detection ◽  
Structure Information ◽  
Finger Vein ◽  
Gray Level Image ◽  
Vein Recognition ◽  
Finger Vein Recognition

Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored localized finger vein images. Then, two kinds of features (gray scale and texture features) are extracted, which tell the structure information of the whole finger vein pattern in the whole dataset. The recurrent based residual neural network (RNN) is used to identify the finger vein images. The experimental show that the localized colored finger vein images based globalized feature extraction has achieved the higher accuracy (93.49%) while the original image dataset achieved less accuracy by (69.86%).

A New Location Method of ROI for Contactless Palm Vein Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1398-1401
Author(s):  
Wei Wu ◽  
Wei Qi Yuan ◽  
Hui Song
Keyword(s):  
Pattern Recognition ◽  
Recognition Rate ◽  
New Method ◽  
Vein Pattern ◽  
Location Method ◽  
Vein Recognition ◽  
Palm Vein ◽  
New Location

Palm vein pattern recognition is one of the newest biometric techniques researched today.At present, literatures selecte the center of the palm as the ROI of palm vein recognition. However the vein image in this area is not clear in some peoples palm. In this paper, we proposed a new location method of ROI which takes thenar area as the ROI. In the experiment part, it compares the recognition rate between the new and the traditional ROI in self-established contactless palm vein database. The result shows that this new method has got the recognition rate of 98.9258% and has increased recognition rate 2.0911% compared with the traditional one.

scholarly journals Finger Vein Recognition Based on (2D)2PCA and Metric Learning

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Gongping Yang ◽  
Xiaoming Xi ◽  
Yilong Yin
Keyword(s):  
Metric Learning ◽  
Recognition Rate ◽  
Class Imbalance ◽  
Biometric Recognition ◽  
Class Imbalance Problem ◽  
Finger Vein ◽  
Imbalance Problem ◽  
Knn Classifier ◽  
Vein Recognition ◽  
Finger Vein Recognition

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

Pengenalan Finger Vein Menggunakan Local Line Binary Pattern dan Learning Vector Quantization

2018 ◽  
Vol 9 (2) ◽  
pp. 52-57
Author(s):  
Jayanti Yusmah Sari ◽  
Rizal Adi Saputra
Keyword(s):  
Feature Extraction ◽  
Vector Quantization ◽  
Recognition System ◽  
Learning Vector Quantization ◽  
Feature Extraction Method ◽  
Finger Vein ◽  
Straight Line ◽  
Vein Recognition ◽  
Form Features ◽  
Finger Vein Recognition

This research proposes finger vein recognition system using Local Line Binary Pattern (LLBP) method and Learning Vector Quantization (LVQ). LLBP is is the advanced feature extraction method of Local Binary Pattern (LBP) method that uses a combination of binary values from neighborhood pixels to form features of an image. The straight-line shape of LLBP can extract robust features from the images with unclear veins, it is more suitable to capture the pattern of vein in finger vein image. At the recognition stage, LVQ is used as a classification method to improve recognition accuracy, which has been shown in earlier studies to show better results than other classifier methods. The three main stages in this research are preprocessing, feature extraction using LLBP method and recognition using LVQ. The proposed methodology has been tested on the SDUMLA-HMT finger vein image database from Shandong University. The experiment shows that the proposed methodology can achieve accuracy up to 90%. Index Terms—finger vein recognition, Learning Vector Quantization, LLBP, Local Line Binary Pattern, LVQ.

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