A Suggested System For Palmprint Recognition Using Curvelet Transform And Co-Occurrence Matrix.

Meaad Alhadidi

doi:10.33899/edusj.2021.130870.1176

Abstract

The main purpose of this paper is to create a palmprint recognition system (PPRS) that uses the curvelet transform and co-occurrence matrix to recognize a hand's palmprint. The suggested system is composed of several stages: in the first stage, the region of interest (ROI) was taken from a palmprint image, then in the second stage, the curvelet transform was applied to the (ROI) to get a blurred version of the image, and finally, unsharp masking process and sobel filtering were done for edge detection. The third stage involves feature extraction using a co-occurrence matrix to obtain 16 features, while the fourth stage inclusion is the training and testing of the suggested approach. The algorithm ACO (ant colony optimization) has been adopted to evaluate the shortest path to the goal. CASIA PalmprintV dataset of 100 people (60 male and 40 female) was used in proposed work to rate the performance of the proposed system. ARR and EER metrics have been adopted to assess the performance of the proposed system. The experimental results showed a very high recognition rate (ARR) that reaches 100% for the right hand of a male and the left hand of a female. The overall accuracy rate (ARR) reaches 98.5% and EER equals 0.015.

Highlights

Biometrics is the study of determining an individual's identity using a vector of information obtained from a behavioral trait or a specific physical property
The algorithm was put to the test on a recognized standard database (CASIA), and the results demonstrate that it is effective in terms of Proper Classification Accuracy, Equal Error Rate, and Computation Time[3]
(1600 images)) which have been used to test the proposed system are from standard Palmprint images (CASIA) which were created by the "Chinese Academy of Sciences Institute of Automation"

Summary

Introduction

Biometrics is the study of determining an individual's identity using a vector of information obtained from a behavioral trait or a specific physical property. Fingerprints, iris, eyes, face, veins, hand, shape, palmprint, and many other physical human features are included in the physiological type. Individuals are recognized in biometrics using behavioral and physiological traits. It provides more security than security solutions that rely on passwords. Biometrics can be used to identify a person's palmprint, fingerprint,hand geometry, iris, face, vocal, signature, and other characteristics[2]. The palm print will be recognized based on extracting features from the palmprint image after enhancing the edges in it by applying many of image processing techniques as well as using the ACO to assess the shortest path to the specific palmprint

Objectives

Results

Conclusion