Volume 2, Issue 1, March 2017, Page: 35-50
Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis
Soltane Mohamed, Electrical Engineering & Computing Department, Faculty of Sciences & Technology, Doctor Yahia Fares University of Medea, Medea, Algeria
Received: Jan. 12, 2017;       Accepted: Feb. 16, 2017;       Published: Mar. 3, 2017
DOI: 10.11648/j.mlr.20170201.15      View  1879      Downloads  340
Abstract
Face recognition has long been a goal of computer vision, but only in recent years reliable automated face recognition has become a realistic target of biometrics research. In this paper the contribution of classifier analysis to the Face Biometrics Verification performance is examined. It refers to the paradigm that in classification tasks, the use of multiple observations and their judicious fusion at the data, hence the decision fusions at different levels improve the correct decision performance. The fusion tasks reported in this work were carried through fusion of two well-known face recognizers, ICA I and ICA II. It incorporates the decision at matching score level, a novel fusion strategy is employed; the Likelihood Ratio Fusion within scores. This strategy increases the accuracy of the face recognition system and at the same time reduces the limitations of individual recognizer. The performance of the analysis studies were tested based on three different face databases ORL 94, Indian face database and eNTERFACE2005 Dynamic Face Database and the simulation results are showed a significant performance achievements.
Keywords
Classifier, Fusion, Biometrics, Face Verification, PCA, LDA, ICA, Likelihood Parameter Estimate
To cite this article
Soltane Mohamed, Pattern Recognition Versus Verification Systems Analysis Studies for Biometrics Face Based Independent Component Analysis, Machine Learning Research. Vol. 2, No. 1, 2017, pp. 35-50. doi: 10.11648/j.mlr.20170201.15
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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