Palm Vein Recognition System Based on Derived Pattern and Feature Vectors

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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An Open Source Contact-Free Palm Vein Recognition System

International Journal of Advances in Applied Sciences ◽

10.11591/ijaas.v6.i4.pp319-324 ◽

2017 ◽

Vol 6 (4) ◽

pp. 319

Author(s):

Ranjith Kumar M ◽

Deepika G G ◽

Meenakshi Krishnan ◽

Karthikeyan B

Keyword(s):

Open Source ◽

Region Of Interest ◽

Recognition System ◽

Computational Time ◽

Raspberry Pi ◽

Support Vector ◽

Vein Recognition ◽

Palm Vein ◽

Open Source Hardware ◽

Time Systems

In this document, we propose a novel palm vein<em> </em>recognition system using open source hardware and software. We have developed an alternative preprocessing and feature extraction technique. The proposed system is built on Raspberry Pi using OpenCV 2.4.12. The palm vein image is cropped to Region of Interest(ROI) to reduce the computational time in real time systems and then preprocessed to enhance the vein pattern visibility and to extract more number of key points using SIFT algorithm. Then the descriptors are stored in a dictionary like codebook file during training. Later the descriptors are tested with unknown patterns. The clustering is based on K-means algorithm and classification is done using Support Vector Machines (SVM).

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A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier

Computational Intelligence and Neuroscience ◽

10.1155/2015/360217 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17 ◽

Cited By ~ 5

Author(s):

Haryati Jaafar ◽

Salwani Ibrahim ◽

Dzati Athiar Ramli

Keyword(s):

Image Enhancement ◽

Current Trend ◽

Recognition Rate ◽

Region Of Interest ◽

Histogram Equalization ◽

Recognition System ◽

Training Data ◽

Hand Tracking ◽

Fast Computation ◽

Palm Print

Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-basedknearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

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Design and implementation of a contactless palm vein recognition system

Proceedings of the 2010 Symposium on Information and Communication Technology - SoICT '10 ◽

10.1145/1852611.1852630 ◽

2010 ◽

Cited By ~ 3

Author(s):

Goh Kah Ong Michael ◽

Tee Connie ◽

Lau Siong Hoe ◽

Andrew Teoh Beng Jin

Keyword(s):

Recognition System ◽

Design And Implementation ◽

Vein Recognition ◽

Palm Vein

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A Contactless Palm Vein Recognition System Based on Principle Line SIFT

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.3469 ◽

2013 ◽

Vol 347-350 ◽

pp. 3469-3472 ◽

Cited By ~ 1

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.

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