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World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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Olajide Blessing Olajide*, Jooda Janet Olubunmi, Adeosun Olajide Olusegun, Odeniyi Olufemi Ayodeji


Face 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.

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