Performance Evaluation Of Selected Principal Component Analysis-Based Techniques For Face Image Recognition
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Date
2015
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH
Abstract
Principal Component Analysis (PCA) is an eigen-based technique popularly employed in redundancy removal and feature extraction for face
image recognition. In this study, performance evaluation of three selected PCA-based techniques was conducted for face recognition. Principal
Component Analysis, Binary Principal Component Analysis (BPCA), and Principal Component Analysis – Artificial Neural Network (PCA-ANN) were
selected for performance evaluation. A database of 400, 50x50 pixels images consisting of 100 different individuals, each individual having 4 images
with different facial expressions was created. Three hundred images were used for training while 100 images were used for testing the three face
recognition systems. The systems were subjected to three selected eigenvectors: 75, 150 and 300 to determine the effect of the size of eigenvectors on
the recognition rate of the systems. The performances of the techniques were evaluated based on recognition rate and total recognition time.The
performance evaluation of the three PCA-based systems showed that PCA – ANN technique 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.
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Aluko, J. O., Omidiora, E. O., Adetunji, A. B. & Odeniyi, O. A. (2015). Performance Evaluation of Selected Principal Component Analysis-Based Techniques for Face Image Recognition, International Journal of Science and Technology Research, Vol. 4, No. 1,