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

The Korean Institute of Electrical Engineers (대한전기학회)
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A Study on Feature Selection in Face Image Using Principal Component Analysis and Particle Swarm Optimization Algorithm

PCA와 입자 군집 최적화 알고리즘을 이용한 얼굴이미지에서 특징선택에 관한 연구

  • 김웅기 (수원대 공대 전기공학과) ;
  • 오성권 (수원대 공대 전기공학과) ;
  • 김현기 (수원대 공대 전기공학과)
  • Published : 2009.12.01
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Abstract

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

Keywords

References

  1. Kirby, M. and Sirovich, L., 'Application of the KL Procedure for the Characterization of Human Faces,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 12, No.1, pp. 103-108, 1990 https://doi.org/10.1109/34.41390
  2. Turk, M. and Pentland, A., 'Eigenfaces for Recognition,' J Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991 https://doi.org/10.1162/jocn.1991.3.1.71
  3. J. Kennedy and R. Eberhart, 'Particle swarm optimization,' Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995 https://doi.org/10.1109/ICNN.1995.488968
  4. M.J. Er, S.Q. Wu, Jw. Lu, H.L. Toh, 'Face recognition with radical 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
  5. Zhou, W. ; Pu, X. & Zheng, Z. 'Parts-Based Holistic Face Recognition with RBF Neural Networks,' In Lecture Notes in Computer Science, (110-115), 3972, 2006 https://doi.org/10.1007/11760023_17
  6. Park, C. ; Ki, M. ; Namkung, J. & Paik, J.K. 'Multimodal Priority Verification of Face and Speech Using Momentum Back-Propagation Neural Network,' In Lecture Notes in Computer Science, (140-149), 3972, 2006 https://doi.org/10.1007/11760023_22
  7. J. Kennedy, 'The particle swarm: Social adaptation of knowledge,' Proc. IEEE Int. Conf. Evolutionary Comput., pp. 303-308, 1997 https://doi.org/10.1109/ICEC.1997.592326
  8. K. E. Parsopoulos and M. N. Vrahatis, 'On the Computation of All Global Minimizers Through Particle Swarm Optimization,' IEEE Trans. Evolutionary Computation, vol. 8, no. 3, pp. 211-224, 2004 https://doi.org/10.1109/TEVC.2004.826076
  9. R.O. Duda, P.E. Hart, D.G. Stork, 'Pattern Classification,' 2nd ed., Wiley-Interscience, 2000