The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Design of Face Recognition algorithm Using PCA＆LDA combined for Data Pre-Processing and Polynomial-based RBF Neural Networks

PCA와 LDA를 결합한 데이터 전 처리와 다항식 기반 RBFNNs을 이용한 얼굴 인식 알고리즘 설계

  • Received : 2011.09.26
  • Accepted : 2012.03.19
  • Published : 2012.05.01
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Abstract

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

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References

  1. A. Patrikar, J. Provence, "Pattern classification using polynomial networks," Electronics Letters, Vol. 28, No. 12, pp. 1109-1110, 1992. https://doi.org/10.1049/el:19920700
  2. M. Turk and A. Pentland, "Eigenfaces for Recognition," Journal of Cognitive Neuroscience, Vol. 3, pp. 71-86, 1994.
  3. J. Moody and C. J. Darken, "Fast learning in network of locally-tuned processing units," Neural Comput., Vol. 1, pp. 281-294, 1989. https://doi.org/10.1162/neco.1989.1.2.281
  4. C. Mazzetti, F. M. Frattale Mascioli, F. Baldini, M. Penella, R. Risica, and R. Bartnikas, "Partial Discharge Pattern Recognition by Neuro-Fuzzy Networks in Heat-Shrinkable Joints and Terminations of XLPE Insulated Distribution Cables," IEEE Trans. on Power Delivery, Vol. 21, No. 3, pp. 1035-1044, 2006. https://doi.org/10.1109/TPWRD.2006.875861
  5. Michalewicz. Z, "Genetic Algorithm + Data Structures = Evolution Programs," Springer-Verlag, Berlin Heidelberg, 1996.
  6. A. Aiyer, K. Pyun, Y. Z. Huang, D. B.. O'Brien, R. M. Gray, "Lloyd clustering of Gauss mixture models for image compression and classufucation," Signal Processing: Image Communication, Vol. 20, pp. 459-485, 2005. https://doi.org/10.1016/j.image.2005.03.003
  7. S. Balakrishnama and A. Ganapathiraju, "LINEAR DISCRIMINANT ANALYSIS A BRIEF TUTORIAL," Institute for Signal and Information Processing, 1998.
  8. Ming-Hsuan Yang, Kernal Eigenfaces vs. Kernal Fisherfaces: Face Recognition Using Kernal Methods, Automatrix Face and Gesture Recognition, 202, Proceedings, Fourth IEEE International Conference on, 2002 Page(s): 208-213.
  9. Maritinez A. M., Kak A. X., "PCA versus LDA," IEEE Trans. on Pattern Analysis and Machine Intellignece, 23(2), pp. 228-232, 2001. https://doi.org/10.1109/34.908974
  10. S. K. Oh, W. Pedrycz, and S. B. Roh, "Genetically Optimized Hybrid Fuzzy Set-based Polynomial Neural Networks," Journal of the Franklin Institute, Vol. 348, No. 2, pp. 415-425, 2011. https://doi.org/10.1016/j.jfranklin.2010.11.005
  11. D. Valentin, H. abdi, A. J. O'Toole, and G. W. Cottrell, "Connectionist models of face processing: A Survey," Pattern Recognition, Vol. 27, pp. 1209-1230, 1994. https://doi.org/10.1016/0031-3203(94)90006-X
  12. M. J. Er, S.Q. Wu, J.W. Lu, H.L. Toh, "Face recognition with radial basis function (RBF) neural networks," IEEE Trans. Neural Networks, Vol. 13, No. 3, pp. 697-710, 2002. https://doi.org/10.1109/TNN.2002.1000134
  13. J. Moody and C. J. Darken, "Fast learning in network of logically-tuned processing units," Neural Comput., Vol. 1, pp. 281-294, 1989. https://doi.org/10.1162/neco.1989.1.2.281
  14. J. C. Bezdek, "Pattern recognition with Fuzzy Objective Function Algorithms," Plenum Press, New York, 1981.
  15. S. K. Oh, W. Pedrycz, and H. S. Park, "Hybrid Identification in Fuzzy-Neural Networks," Fuzzy Sets and Systems, Vol. 138, Issue 2, pp. 399-426, 2003. https://doi.org/10.1016/S0165-0114(02)00441-4
  16. R. Storn, Differential Evolution, A Simple and Efficient Heuristic Strategy for Global Optimization over Continuous Spaces, Journal of Global Optimization, Vol. 11, pp. 341-359, 1997. https://doi.org/10.1023/A:1008202821328