Parametic Classification of Handvein Patterns Based on Texture Features

TL;DR

This paper presents a biometric handvein recognition system using texture feature extraction algorithms and classifiers, demonstrating improved performance through feature fusion.

Contribution

It introduces a combination of texture-based feature extraction methods and classifiers for handvein recognition, with a focus on feature fusion to enhance accuracy.

Findings

Feature fusion improves recognition accuracy.

LPQ and Log-Gabor descriptors perform well.

SVM and KNN classifiers achieve high recognition rates.

Abstract

In this paper, we have developed Biometric recognition system adopting hand based modality Handvein, which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor ,LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.