Multispectral Palmprint Recognition Using Textural Features

TL;DR

This paper introduces a novel multispectral palmprint recognition method using textural features extracted via co-occurrence matrices, achieving near-perfect accuracy and outperforming previous approaches.

Contribution

It presents a new approach utilizing textural features and co-occurrence matrices for palmprint recognition, demonstrating superior accuracy over existing methods.

Findings

Achieved 99.96-100% accuracy on a multispectral palmprint dataset.

Outperformed all previous multispectral palmprint recognition methods.

Validated effectiveness of textural features for biometric identification.

Abstract

In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features and used them for palmprint recognition. Co-occurrence matrix can be used for textural feature extraction. As classifiers, we have used the minimum distance classifier (MDC) and the weighted majority voting system (WMV). The proposed method is tested on a well-known multispectral palmprint dataset of 6000 samples and an accuracy rate of 99.96-100% is obtained for most scenarios which outperforms all previous works in multispectral palmprint recognition.