Palm Vein Identification based on hybrid features selection model

TL;DR

This paper introduces a hybrid feature selection model combining 2D-DWT, PCA, and PSO to improve palm vein identification accuracy, outperforming existing methods like AlexNet and non-feature-selected classifiers.

Contribution

The paper presents a novel hybrid feature selection approach for palm vein identification that significantly enhances accuracy over existing models.

Findings

Achieved 98.65% accuracy with the proposed model.

Outperformed AlexNet and non-feature-selected classifiers.

Demonstrated the effectiveness of combining 2D-DWT, PCA, and PSO.

Abstract

Palm vein identification (PVI) is a modern biometric security technique used for increasing security and authentication systems. The key characteristics of palm vein patterns include, its uniqueness to each individual, unforgettable, non-intrusive and cannot be taken by an unauthorized person. However, the extracted features from the palm vein pattern are huge with high redundancy. In this paper, we propose a combine model of two-Dimensional Discrete Wavelet Transform, Principal Component Analysis (PCA), and Particle Swarm Optimization (PSO) (2D-DWTPP) to enhance prediction of vein palm patterns. The 2D-DWT Extracts features from palm vein images, PCA reduces the redundancy in palm vein features. The system has been trained in selecting high reverent features based on the wrapper model. The PSO feeds wrapper model by an optimal subset of features. The proposed system uses four classifiers as an objective function to determine VPI which include Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT) and Na\"ive Bayes (NB). The empirical result shows the proposed system Iit satisfied best results with SVM. The proposed 2D-DWTPP model has been evaluated and the results shown remarkable efficiency in comparison with Alexnet and classifier without feature selection. Experimentally, our model has better accuracy reflected by (98.65) while Alexnet has (63.5) and applied classifier without feature selection has (78.79).