Comparative analysis of Palm-Vein recognition system using basic transforms

2015 ◽  
Author(s):  
Sonali Vaid ◽  
Dhirendra Mishra
Keyword(s):  
Comparative Analysis ◽  
Recognition System ◽  
Vein Recognition ◽  
Palm Vein

A Contactless Palm Vein Recognition System Based on Principle Line SIFT

2013 ◽  
Vol 347-350 ◽  
pp. 3469-3472 ◽  
Author(s):  
Wei Wu ◽  
Sen Lin ◽  
Hui Song
Keyword(s):  
Traditional Method ◽  
Recognition System ◽  
Scale Invariant ◽  
Limited Effect ◽  
Image Deformation ◽  
Invariant Feature ◽  
Feature Transform ◽  
Vein Recognition ◽  
Palm Vein ◽  
Scale Invariant Feature

Compared with the traditional method of contact collection, contactless acquisition is the mainstream and trend of palm vein recognition. However, this method may lead to image deformation caused by no parallel of the palm plane and the sensor plane. In order to improve the limited effect of Scale Invariant Feature Transform (SIFT) about this problem, a better method of palm vein recognition which based on principle line SIFT is proposed. Based on the self-built database, this method is compared with the SIFT and other typical palm vein recognition methods, the experimental results show that our system can achieve the best performance.

A Palm Vein Recognition System based on a Support Vector Machine

2019 ◽  
Vol 8 (1) ◽  
pp. 1-7
Author(s):  
Vijayakumar Ponnusamy ◽  
Abhijit Sridhar ◽  
Arun Baalaaji ◽  
M. Sangeetha
Keyword(s):  
Support Vector Machine ◽  
Recognition System ◽  
Support Vector ◽  
Vein Recognition ◽  
Palm Vein

An extensive survey on finger and palm vein recognition system

2020 ◽  
Author(s):  
Rama Vasantha Adiraju ◽  
Kranthi Kumar Masanipalli ◽  
Tamalampudi Deepak Reddy ◽  
Rohini Pedapalli ◽  
Sindhu Chundru ◽  
...  
Keyword(s):  
Recognition System ◽  
Extensive Survey ◽  
Vein Recognition ◽  
Palm Vein

Palm Vein Recognition Algorithm Using Curvelet and Wavelet

2013 ◽  
Vol 710 ◽  
pp. 655-659
Author(s):  
Zhi Xian Jiu ◽  
Qiang Li
Keyword(s):  
Wavelet Transform ◽  
Error Rate ◽  
Minimum Distance ◽  
Image Database ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Equal Error Rate ◽  
Vein Recognition ◽  
Palm Vein

In this paper we report on a curvelet and wavelet based palm vein recognition algorithm. Using our palm vein image database, we employed minimum distance classifier to test the performance of the system. Experimental results show that the algorithm based on cuvelet transform can reach equal error rate of 1.7%, and the algorithm based on wavelet transform can only reach equal error rate of 2.3%, indicating that the curvelet based palm vein recognition system improves representation.

WAVELET-BASED PALM VEIN RECOGNITION SYSTEM

2010 ◽  
Vol 03 (02) ◽  
pp. 131-134
Author(s):  
QIANG LI ◽  
YAN'AN ZENG ◽  
KUNTAO YANG
Keyword(s):  
Nearest Neighbor ◽  
Recognition Rate ◽  
Texture Feature ◽  
Recognition System ◽  
Vein Pattern ◽  
Personal Recognition ◽  
Vein Recognition ◽  
Palm Vein ◽  
Correct Recognition Rate ◽  
First Time

A new personal recognition system using the palm vein pattern is presented in this article. It is the first time that the palm vein pattern is used for personal recognition. The texture feature of palm vein is extracted by wavelet decomposition. With our palm vein image database, we employed the nearest neighbor (NN) classifier to test the performance of the system. Experimental results show that the algorithm based on wavelet transform can reach a correct recognition rate (CRR) of 98.8%.

Palm Vein Recognition System Based on Derived Pattern and Feature Vectors

2017 ◽  
Vol 8 (2) ◽  
pp. 56-72 ◽  
Author(s):  
Aderonke Lawal ◽  
Segun Aina ◽  
Samuel Okegbile ◽  
Seun Ayeni ◽  
Dare Omole ◽  
...  
Keyword(s):  
Region Of Interest ◽  
High Sensitivity ◽  
Histogram Equalization ◽  
Recognition System ◽  
Thinning Algorithm ◽  
Feature Vectors ◽  
Palm Print ◽  
Vein Recognition ◽  
High Recognition Accuracy ◽  
Palm Vein

Biometrics is a technology for recognition under which Palm vein recognition stems. They are of crucial importance in various applications of high sensitivity. This article develops a palm vein recognition model, based on derived pattern and feature vectors. All the palm print images used in this work were obtained from CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database). First, a Region of Interest (ROI) was identified and extracted from the palm print images. Next, Histogram Equalization was used to enhance the area of the palm print image in the Region of Interest. The enhanced image obtained was subjected to the Zhang Suen's Thinning Algorithm to extract appropriate features in the palm print images needed for authentication. The features derived based on this vascular pattern thinning algorithm which are then compared and evaluated to carry out ‘matching'. The Pattern Matching itself was done using the Euclidean Distance for subsequent matching. The model was designed using UML, and implemented with C# and MS SQL on Microsoft Visual Studio platform. The developed system was evaluated based on False Acceptance, False Rejection and Equal Error Rate (EER) values obtained from the system. The results of testing and evaluation show that the developed system has achieved high recognition accuracy.

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