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A Genetic Programming-PCA Hybrid Face Recognition Algorithm
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Journal of Signal and Information Processing·Volume 02 (2011)·Pages 170–174·Published 31 August 2011·DOI10.4236/jsip.2011.23022
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Abstract
Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also utilized. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions.
KeywordsFace RecognitionPrincipal Component AnalysisGenetic ProgrammingLeveraging Algorithm
- S. Liu, Y. Tian and D. Li, “New research Advances of Facial Expression Recognition,” International Conference on Machine Learning and Cybernetics, Baoding, Vol. 2, July 2009, pp. 1150-1155.
- I. T. Jolliffe, “Principal Component Analysis,” Springer-Verlag New York, Inc., 2002.
- M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86.
- A. Pentland, B. Moghaddam and T. Starner, “View-Based and Modular Eigenspaces for Face Recognition,” Proceedings CVPR’94, 1994 IEEE Computer Society Conference on, Seattle, July 1994, pp. 84-91.
- A. Eleyan and H. Demirel, “PCA and LDA Based Face Recognition Using Feedforward Neural Network Classifier,” Lecture Notes in Computer Science, Vol. 4105, 2006, pp. 199-206. doi:10.1007/11848035_28
- J. R. Koza, “Genetic Programming: On the Programming of Computer by Means of Natural Selection,” MIT Press: Cambridge, 1992.
- S. Xuesong and Y. Zhou, “Gray Intensity Images Processing for PD Pattern Recognition Based on Genetic Programming,” International Joint Conference on Artificial Intelligence JCAI’09, Haikou, 2009, pp. 711-714.
- A. Teredesai and V. Govindaraju, “Issues in Evolving GP Based Classifiers for a Pattern Recognition Task,” Proceedings of the 2004 IEEE Congress on Evolutionary Computation, 20-23 June 2004, pp. 509-515.
- J. R. Koza, M. A. Keane, M. J. Streeter, W. Mydlowec, J. Yu and G. Lanza, “Genetic Programming IV: Routine Human-Competitive Machine Intelligence,” Kluwer Academic Publishers, Norwell, 2003.
- N. Krause and Y. Singer, “Leveraging the Margin More Carefully,” Proceedings of the Twenty-First International Conference on Machine Learning, Banff, 2004, p. 63.
- J. K. Sing, S. Thakur, D. K. Basu and M. Nasipuri1, “Direct Kernel PCA with RBF Neural Networks for Face Recognition,” IEEE TENCON Region 10 Conference, Hyderabad, 2008, pp. 1-6.
- AT&T, “The Database of Faces,” 2011. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatase.html.
- Y. Q. Pan and Y. Liu, “Face Recognition Using Kernel PCA and Hybrid Flexible Neural Tree,” Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, 2-4 November 2007, pp. 1361-1366.
- J. Wang, Y. Chen and M. Adjouadi, “A Comparative Study of Multilinear Principal Component Analysis for Face Recognition,” 37th IEEE Applied Image Pattern Recognition Workshop, 2008, pp. 1-6. doi:10.1109/AIPR.2008.4906476