Influence of Eigenvector on Selected Facial Biometric Identification Strategies
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Date
2020-02-16
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Publisher
World Journal of Engineering Research and Technology
Abstract
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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Keywords
Biometric, Total Recognition Time, Identification, Principal Component Analysis, Artificial Neural Network, Recognition Rate,