Biometric Authentication Based on Hand Vein Pattern

Recently, a new biometric technology based on human hand vein patterns has attracted the attention of many researchers. This chapter discusses vein pattern authentication, which uses the vascular patterns of the back of the hand as personal authentication data. Vein information is hard to duplicate because veins are internal to the human body. Vein authentication is one of the most accurate and reliable biometric technologies, which is widely employed in mission-critical applications such as banking, etc. A dynamic ROI extraction algorithm was presented through which more features can be extracted when compared to the fixed ROI. The extracted ROI was enhanced, and then the noise content was removed. The key features that represent the geometric information of the vein pattern were extracted; they are the bifurcation and ending points. This chapter presents a new vein pattern recognition system by assigning different weights to bifurcation and ending points. The approach is tested on a vein pattern database of 60 different hands. Experimental results show the approach achieves 2.5% of Equal Error Rate (EER) and recognition accuracy of 98.24%.

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Dorsal hand vein pattern recognition system based on neural network

2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) ◽

10.1109/iceca.2017.8212720 ◽

2017 ◽

Cited By ~ 1

Author(s):

Aishwarya S Bhosale ◽

Maruti R Jadhav

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Recognition System ◽

Vein Pattern ◽

Pattern Recognition System ◽

Dorsal Hand ◽

Hand Vein ◽

Dorsal Hand Vein

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Enhancing Fixed Size Palmprint Region of Interest (ROI) Extraction Algorithm for Personal Identification

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d5177.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 6918-6923

Keyword(s):

Region Of Interest ◽

Recognition System ◽

Small Region ◽

Personal Identification ◽

Fixed Size ◽

Extraction Area ◽

Extraction Region ◽

Extraction Algorithm ◽

Roi Extraction ◽

Biometrics Recognition

Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint that can be used in the identification process. However, only a small region of the palmprint can be extracted using the existing palmprint region of interest (ROI) extraction algorithms. This has become a problem for identification systems due to negligible and loss of important features which are located outside the ROI. Hence, it is a necessity to improve the palmprint ROI extraction algorithm whereby bigger palmprint ROI can be extracted using this algorithm. Therefore, a larger fixed size extraction algorithm for palmprint ROI is proposed where the extraction region is larger so that more important identification features can be captured inside these ROIs. The performance between proposed and existing extraction algorithms are tested based on two characteristics which are the palmprint ROI extraction area and the comparison of feature creases extracted in a palmprint ROI. The results show that 300x300 fixed size ROI is able to capture 13 out of 14 creases attributes for palmprint identification. This implies that the proposed extraction algorithm shows a promising method of extraction as compared to the existing algorithms.

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Biometric Recognition System Based on Hand Vein Pattern

Acta Optica Sinica ◽

10.3788/aos20092912.3339 ◽

2009 ◽

Vol 29 (12) ◽

pp. 3339-3343 ◽

Cited By ~ 3

Author(s):

刘铁根 Liu Tiegen ◽

王云新 Wang Yunxin ◽

李秀艳 Li Xiuyan Jiang ◽

江俊峰 Junfeng ◽

周苏晋 Zhou Sujin

Keyword(s):

Recognition System ◽

Biometric Recognition ◽

Vein Pattern ◽

Hand Vein

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Near infrared hand vein image acquisition and ROI extraction algorithm

Optik ◽

10.1016/j.ijleo.2015.09.001 ◽

2015 ◽

Vol 126 (24) ◽

pp. 5682-5687 ◽

Cited By ~ 3

Author(s):

Qi Zhu ◽

Zheng Zhang ◽

Ningzhong Liu ◽

Han Sun

Keyword(s):

Near Infrared ◽

Image Acquisition ◽

Hand Vein ◽

Extraction Algorithm ◽

Roi Extraction

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GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i2.10347 ◽

2020 ◽

Vol 5 (2) ◽

pp. 609

Author(s):

Segun Aina ◽

Kofoworola V. Sholesi ◽

Aderonke R. Lawal ◽

Samuel D. Okegbile ◽

Adeniran I. Oluwaranti

Keyword(s):

Support Vector Machine ◽

Gesture Recognition ◽

Recognition Rate ◽

Recognition Task ◽

Recognition System ◽

Human Interaction ◽

Support Vector ◽

System A ◽

Extraction Algorithm ◽

Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

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An Efficient Matching Classifier for Hand Vein Pattern Recognition in User Authentication Systems

i-manager’s Journal on Future Engineering and Technology ◽

10.26634/jfet.4.3.274 ◽

2009 ◽

Vol 4 (3) ◽

pp. 13-22

Author(s):

S. Uma Maheswari ◽

◽

P. Anbalagan ◽

Keyword(s):

Pattern Recognition ◽

User Authentication ◽

Vein Pattern ◽

Hand Vein ◽

Efficient Matching

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Bimodal Hand Vein Recognition System using Support Vector Machine

2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) ◽

10.1109/hnicem51456.2020.9400017 ◽

2020 ◽

Author(s):

Abram Philip I D. Magadia ◽

Rufter Fits Gerald L. Zamora ◽

Noel B. Linsangan ◽

Hanna Leah P. Angelia

Keyword(s):

Support Vector Machine ◽

Recognition System ◽

Support Vector ◽

Hand Vein ◽

Vein Recognition

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A FULLY AUTOMATED OFFLINE HANDWRITING RECOGNITION SYSTEM INCORPORATING RULE BASED NEURAL NETWORK VALIDATED SEGMENTATION AND HYBRID NEURAL NETWORK CLASSIFIER

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001404003654 ◽

2004 ◽

Vol 18 (07) ◽

pp. 1267-1283 ◽

Cited By ~ 5

Author(s):

MOUMITA GHOSH ◽

RANADHIR GHOSH ◽

BRIJESH VERMA

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Solution Method ◽

Handwriting Recognition ◽

Recognition System ◽

Neural Network Classifier ◽

Hybrid Neural Network ◽

Rule Based ◽

Extraction Algorithm ◽

Offline Handwriting Recognition

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

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A New Preprocessing Algorithm of Hand Vein Image

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.462-463.312 ◽

2013 ◽

Vol 462-463 ◽

pp. 312-315

Author(s):

Cai Xia Liu

Keyword(s):

Wavelet Decomposition ◽

Feature Recognition ◽

Hot Spot ◽

Low Frequency ◽

Histogram Equalization ◽

Segmentation Algorithm ◽

Human Hand ◽

Security Technology ◽

Hand Vein ◽

Vein Recognition

Biometrics technology is an important security technology and the research of it has become a new hot spot for its superior security features. Then hand vein recognition is a new biological feature recognition which has many advantages, such as safety, non-contact. According to the features of human hand vein image, a hand vein preprocessing method based on wavelet transform and windows maximum between-class difference method threshold (OTSU) segmentation algorithm is proposed. In this paper, the hand vein image is enhanced by adaptive histogram equalization in low frequency part of the hand vein image after wavelet decomposition and filtering before feature extraction. Then the windows OTSU threshold segmentation algorithm is used to get the features. The experimental results show that this method is simple and easy to realize and has laid a good foundation for the latter part of the vein recognition.

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