COMPREHENSIVE EVALUATION OF APPEARANCE-BASED TECHNIQUES FOR PALMPRINT FEATURES EXTRACTION USING PROBABILISTIC NEURAL NETWORK, COSINE MEASURES AND EUCLIDEAN DISTANCE CLASSIFIERS

Akande, N.O and Abikoye, O.C. and Adeyemo, I.A. and Ogundokun, Roseline Oluwaseun and Aro, T.O. (2018) COMPREHENSIVE EVALUATION OF APPEARANCE-BASED TECHNIQUES FOR PALMPRINT FEATURES EXTRACTION USING PROBABILISTIC NEURAL NETWORK, COSINE MEASURES AND EUCLIDEAN DISTANCE CLASSIFIERS. Editura Universitatii din Pitesti, 18 (1). pp. 6-14. ISSN 1453 – 1119

[img] Text
PalmPrint feature extraction Akande.pdf - Published Version

Download (1MB)

Abstract

Most biometric systems work by comparing features extracted from a query biometric trait with those extracted from a stored biometric trait. Therefore, to a great extent, the accuracy of any biometric system is dependent on the effectiveness of its features extraction stage. With an intention to establish a suitable appearance based features extraction technique, an independent comparative study of Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) algorithms for palmprint features extraction is reported in this article. Euclidean distance, Probabilistic Neural Network (PNN) and cosine measures were used as classifiers. Results obtained revealed that cosine metrics is preferable for ICA features extraction while PNN is preferable for LDA features extraction. Both PNN and Euclidean distance yielded a better recognition rate for PCA. However, ICA yielded the best recognition rate in terms of FAR and FRR followed by LDA then PCA.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Depositing User: Mr DIGITAL CONTENT CREATOR LMU
Date Deposited: 31 Oct 2019 11:15
Last Modified: 31 Oct 2019 11:15
URI: https://eprints.lmu.edu.ng/id/eprint/2712

Actions (login required)

View Item View Item