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An Overview of Principal Component Analysis

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DOI: 10.4236/jsip.2013.43B031    8,654 Downloads   12,768 Views   Citations

ABSTRACT

The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

S. Karamizadeh, S. Abdullah, A. Manaf, M. Zamani and A. Hooman, "An Overview of Principal Component Analysis," Journal of Signal and Information Processing, Vol. 4 No. 3B, 2013, pp. 173-175. doi: 10.4236/jsip.2013.43B031.

References

[1] J. Ashok, V. Shivashankar and P. Mudiraj, “An Overview of Biometrics,” International Journal, Vol. 2, 2010.
[2] F. A–zen, “A Face Recognition System Based on Eigenfaces Method,” Procedia Technology, Vol. 1, 2011, pp. 118-123.
[3] W. Miziolek and D. Sawicki, “Face Recognition: PCA or ICA,” Przeglad Elektrotechniczny, Vol. 88, 2012, pp. 286-288.
[4] C. Li, Y. Diao, H. Ma and Y. Li, “A Statistical PCA Method for Face Recognition,” in Intelligent Information Technology Application, 2008, pp. 376-380.
[5] H. Duan, R. Yan and K. Lin, “Research on Face Recognition Based on PCA,” in Future Information Technology and Management Engineering, 2008, pp. 29-32.
[6] T. F. Karim, M. S. H. Lipu, M. L. Rahman and F. Sultana, “Face Recognition Using PCA-based Method,” in Advanced Management Science (ICAMS), 2010 IEEE International Conference on, 2010, pp. 158-162.
[7] Z. Haiyang, “A Comparison of PCA and 2DPCA in Face Recognition,” in Electrical Power Systems and Computers: Springer, Vol. 99, 2011, pp. 445-449. doi:10.1007/978-3-642-21747-0_55
[8] B. Poon, M. A. Amin and H. Yan, “PCA based Face Recognition and Testing Criteria,” in Machine Learning and Cybernetics, 2009 International Conference on, 2009, pp. 2945-2949.
[9] Y. Wen and P. Shi, “Image PCA: A New Approach for Face Recognition,” in Acoustics, Speech and Signal Processing, IEEE International Conference on, 2007, pp. 1241-1244.
[10] A. Boualleg, C. Bencheriet and H. Tebbikh, “Automatic Face Recognition Using Neural Network-PCA,” in Information and Communication Technologies, 2006, pp. 1920-1925.
[11] Z. Wang and X. Li, “Face Recognition Based on Improved PCA Reconstruction,” in Intelligent Control and Automation (WCICA), 2010 8th World Congress on, 2010, pp. 6272-6276.
[12] J. Meng and Y. Yang, “Symmetrical Two-Dimensional PCA with Image Measures in Face Recognition,” Int J Adv Robotic Sy, Vol. 9, 2012.
[13] D. S. Pankaj and M. Wilscy, “Comparison of PCA, LDA and Gabor Features for Face Recognition Using Fuzzy Neural Network,” in Advances in Computing and Information Technology: Springer, 2013, pp. 413-422.
[14] M. Safayani, M. T. Manzuri Shalmani and M. Khademi, “Extended Two-dimensional Pca for Efficient Face Representation and Recognition,” in Intelligent Computer Communication and Processing, 2008. ICCP 2008. 4th International Conference on, 2008, pp. 295-298.
[15] L. Sirovich and M. Kirby, “Low-dimensional Procedure for the Characterization of Human Faces,” Vol. 4, No. 3, 1987, pp. 519-524. doi:10.1364/JOSAA.4.000519
[16] M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp. 71-86. doi:10.1162/jocn.1991.3.1.71
[17] “Citation Report Title=(PCA face recognition)” Vol. 2013, 2013.
[18] K. S. Sodhi and M. Lal, “Face Recognition Using PCA, LDA and Various Distance Classifier,” Journal of Global Re-search in Computer Science, Vol. 4, 2013, pp. 30-35.
[19] P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min and W. Worek, “Overview of the Face Recognition Grand Challenge,” in Computer vision and pattern recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, pp. 947-954.
[20] D. Srinivasulu Asadi, Ch. DV Subba Rao and V. Saikrishna “A Comparative Study of Face Recognition with Principal Component Analysis and Cross-Correlation Technique,” International Journal of Computer Applications Vol. 10, 2010.

  
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