Influence of Eigenvector on Selected Facial Biometric Identification Strategies

dc.contributor.authorJooda, Janet
dc.date.accessioned2022-05-04T08:12:27Z
dc.date.available2022-05-04T08:12:27Z
dc.date.issued2020-02-16
dc.description.abstractFace identification strategies are becoming more popular among biometric-based strategies as it measures an individual‟s natural data to authenticate and identify individuals by analyzing their physical characteristics. For face identification system to be efficient and robust to serve it purpose of security, there is need to use the best strategy out of the many strategies that have been proposed in literatures for face identification. Amidst the most popularly used face identification strategies, Principal Component Analysis PCA, Binary Principal Component Analysis BPCA, and Principal Component Analysis – Artificial Neural Network PCA-ANN were selected for performance evaluation. The research was experimented by varying the eigenvector of the training images for each strategy to compare the performance using Recognition Rate RR and Total Recognition Time TR as performance metrics. Results showed that PCA – ANN strategy gave the best recognition rate of 94% with a trade-off in recognition time. Also, the recognition rates of PCA and B-PCA increased with decreasing number of eigenvectors but PCA-ANN recognition rate was negligible. Hence PCA-ANN outperforms the other face identification strategies.en_US
dc.identifier.issn2454-695X
dc.identifier.urihttp://dspace.run.edu.ng:8080/jspui/handle/123456789/2594
dc.language.isoenen_US
dc.publisherWorld Journal of Engineering Research and Technologyen_US
dc.relation.ispartofserieswjert, 2020, Vol. 6, Issue 2;39-53
dc.subjectBiometricen_US
dc.subjectTotal Recognition Timeen_US
dc.subjectIdentificationen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectArtificial Neural Networken_US
dc.subjectRecognition Rate,en_US
dc.titleInfluence of Eigenvector on Selected Facial Biometric Identification Strategiesen_US
dc.typeArticleen_US
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