Comprehensive Evaluation of Appearance-Based Techniques for Palmprint Features Extraction Using Probabilistic Neural Network, Cosine Measures and Euclidean Distance Classifiers
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
Editura Universitatii din Pitesti
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.
Description
Keywords
Cosine Measures, Probabilistic Neural Network, Principal Component Analysis, Euclidean Distance, Independent Component Analysis, Linear Discriminant Analysis, Palmprint Feature Extraction
Citation
Akande N. O., Abikoye O. C., Adeyemo I. A., Ogundokun R. O. & Aro T. O.(2018): Comprehensive Evaluation of Appearance-Based Techniques for Palmprint Features Extraction using Probabilistic Neural Network, Cosine Measures and Euclidean Distance Classifiers . The University of Pitesti Scientific Bulletin, Series: Electronics and Computers Science, 18(1); 5- 14, Published by Editura Universitatii din Pitesti.