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IJSTR >> Volume 4 - Issue 1, January 2015 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Performance Evaluation Of Selected Principal Component Analysis-Based Techniques For Face Image Recognition

[Full Text]

 

AUTHOR(S)

Aluko J. Olubunmi, Omidiora E. Olusayo, Adetunji A. Bola, Odeniyi O. Ayodeji

 

KEYWORDS

Index terms: Principal Component Analysis, Binary Principal Component Analysis (BPCA), and Principal Component Analysis – Artificial Neural Network (PCA-ANN).

 

ABSTRACT

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.

 

REFERENCES

[1] Agarwal, M., Jain, N., Kumar M. and Agrawal H. (2010): “Face Recognition Using Eigen Faces and Artificial Neural Network”. International Journal of Computer Theory and Engineering, 2(4): pp. 624-629.

[2] Bevilacqua, V., Mastronardi, G., Pedone, G., Romanazzi, G., and Paleno, D. (2006): “Hidden Markov Model for Recognition Using Artificial Neural networks”. Springer-Verlag, Heidelberg, New York, 19(1): pp. 8-9.

[3] Jain K. and Singh S. (2011): “Performance Evaluation of Face Recognition Using PCA”. International Journal of Information Technology and Knowledge Management, 4(2): pp. 427-430

[4] Latha, P., Dr. Ganesan L. and Dr. Annadurai S.: “Face Recognition Using Neural Networks”. An International Journal (SPIJ) 3(5): pp. 155-157

[5] Martinez, A.M. and Kak, A.C. (2001): “PCA versus LDA”. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(2): pp. 228-233.

[6] Nazeer S.A and Khalid M. (2009): “PCA-ANN Face Recognition System based on Photometric Normalization Techniques”. ISBN-3-902613-42-4, pp. 250, I-Tech, Vienna, Austria.

[7] Omidiora E.O., Fakolujo O.A., Ayeni R.O., Olabiyisi S.O., and Arulogun O.T. (2008): “Quantitative Evaluation of Principal Component Analysis and Fisher Discriminant Analysis Techniques in Face images”. Journal of Computer and its Applications, 15(1): pp. 22-37.

[8] Tang F. and Tao H. (2007): “Representing Images Using Non-orthogonal Haar-Like Bases”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12): pp.2120-2133.

[9] Tao H., Crabb R., and Tang F. (2005): “Non-Orthogonal Binary Subspace and its Applications in Computer Vision”. In ICCV, pp. 864–870.

[10] Turk M.A. and Petland A.P. (1991): “Eigenfaces for Recognition”. Journal of Cognitive Neuroscience. 3(1): pp.71-86.

[11] Wilson, P.I. and Fernandez, J. (2006): “Facial Feature Detection Using Haar Classifiers”. J. Comput. Small Coll. 21(4): pp. 127-133.