Structure Feature Extraction for Finger-Vein Recognition

2013 ◽  
Author(s):  
Di Cao ◽  
Jinfeng Yang ◽  
Yihua Shi ◽  
Chenghua Xu
Keyword(s):  
Feature Extraction ◽  
Finger Vein ◽  
Structure Feature ◽  
Vein Recognition ◽  
Finger Vein Recognition

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%).

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.

scholarly journals Finger-Vein Recognition Using Bidirectional Feature Extraction and Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiyong Tao ◽  
Xinru Zhou ◽  
Zhixue Xu ◽  
Sen Lin ◽  
Yalei Hu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Feature Recognition ◽  
Recognition Performance ◽  
Support Vector ◽  
Position Information ◽  
Finger Vein ◽  
Security Research ◽  
Vein Recognition ◽  
Finger Vein Recognition

Accuracy and efficiency are essential topics in the current biometric feature recognition and security research. This paper proposes a deep neural network using bidirectional feature extraction and transfer learning to improve finger-vein recognition performance. Above all, we make a new finger-vein database with the opposite position information of the original one and adopt transfer learning to make the network suitable for our overall recognition framework. Next, the feature extractor is constructed by adjusting the unidirectional database’s parameters, capturing vein features from top to bottom and vice versa. Correspondingly, we concatenate the above two features to form the finger-veins’ bidirectional features, which are trained and classified by Support Vector Machines (SVM) to realize recognition. Experiments are conducted on the Malaysian Polytechnic University’s published database (FV-USM) and finger veins of Signal and Information Processing Laboratory (FV-SIPL). The accuracy of our proposed algorithm reaches 99.67% and 99.31%, which is significantly higher than the unidirectional recognition under each database. Compared with the algorithms cited in this paper, our proposed model based on bidirectional feature enjoys higher accuracy, faster recognition speed than the state-of-the-art frameworks, and excellent practical value.

scholarly journals A Systematic Review of Finger Vein Recognition Techniques

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 213 ◽  
Author(s):  
Kashif Shaheed ◽  
Hangang Liu ◽  
Gongping Yang ◽  
Imran Qureshi ◽  
Jie Gou ◽  
...  
Keyword(s):  
Systematic Review ◽  
Feature Extraction ◽  
Comparative Analysis ◽  
Biometric Identification ◽  
Identification Methods ◽  
Detailed Review ◽  
Finger Vein ◽  
Behavioral Attributes ◽  
Vein Recognition ◽  
Finger Vein Recognition

Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

scholarly journals Improving the Performance of Finger-Vein Recognition System Using A New Scheme of Modified Preprocessing Methods

2020 ◽  
Vol 9 (3) ◽  
pp. 397
Author(s):  
Ahmed A. Mustafa ◽  
Ahmed AK. Tahir
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Extraction Methods ◽  
Error Rates ◽  
Least Square ◽  
Base Line ◽  
Finger Vein ◽  
Roi Extraction ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims at improving the performance of finger-vein recognition system using a new scheme of image preprocessing. The new scheme includes three major steps, RGB to Gray conversion, ROI extraction and alignment and ROI enhancement. ROI extraction and alignment includes four major steps. First, finger-vein boundaries are detected using two edge detection masks each of size (4 x 6). Second, the correction for finger rotation is done by calculating the finger base line from the midpoints between the upper and lower boundaries using least square method. Third, ROI is extracted by cropping a rectangle around the center of the finger-vein which is determined using the first and second invariant moments. Forth, the extracted ROI is normalized to a unified size of 192 x 64 in order to compensate for scale changes. ROI enhancement is done by applying the technique of Contrast-Limited Adaptive Histogram Equalization (CLAHE), followed by median and modified Gaussian high pass filters. The application of the given preprocessing scheme to a finger-vein recognition system revealed its efficiency when used with different methods of feature extractors and with different types of finger-vein database. For the University of Twente Finger Vascular Pattern (UTFVP) database, the achieved Identification Recognition Rates (IRR) for identification mode using three feature extraction methods Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Line Binary Pattern (LLBP) are (99.79, 99.86 and 99.86) respectively, while the achieved Equal Error Rates (EER) for verification mode for the same feature extraction methods are (0.139, 0.069 and 0.035). For the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) database, the achieved Identification Recognition Rates (IRR) for identification mode using three feature extraction methods LBP, LDP and LLBP are (99.57, 99.73 and 99.65) respectively, while the achieved Equal Error Rates (EER) for verification mode for the same feature extraction methods are (0.419, 0.262 and 0.341). These results outrage those of the previous state-of-art methods.

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