C2DPCA & R2DLDA for Face Recognition

얼굴 인식 시스템을 위한 C2DPCA & R2DLDA

  • Received : 2009.12.23
  • Accepted : 2010.07.21
  • Published : 2010.08.28


The study has proposed a method that simultaneously takes advantage of each projection matrix acquired by using column-directional two-dimensional PCA(C2DPCA) and row-directional two-dimensional LDA(R2DLDA). The proposed method can acquire a great secure recognition rate, with no relation to the number of training images, with acquired low-dimensional feature matrixes including both the horizontal and the vertical features of a face. Besides, in the alternate experiment of PCA and LDA to row-direction and column-direction respectively(C2DPCA & R2DLDA, C2DLDA & R2DPCA), we could make sure the system of 2 dimensional LDA with row-directional feature(C2DPCA & R2DLDA) obtain higher recognition rate with low dimension than opposite case. As a result of experimenting that, the proposed method has showed a greater recognition rate of 99.4% than the existing methods such as 2DPCA and 2DLDA, etc. Also, it was proved that its recognition processing is over three times as fast as that of 2DPCA or 2DLDA.


2DPCA;2DLDA;LDA;PCA;Face Recognition


  1. T. Kanade, "Picture processing by computer complex and recognition of human faces," Ph.D thesis, Kyoto University, 1973.
  2. M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, Vol.3, No.1, pp.71-86, 1991.
  3. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.19, No.7, pp.711-720, 1997.
  4. J. Yang, D. Zhang, A. F. Frangi, and J. Yang,"Two-dimensional PCA: a new approach to appearance-based face representation and recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.26, No.1, pp.131-137, 2004.
  5. M. Li and B. Yuan, "2D-LDA: A statistical linear discriminant analysis for image matrix," Pattern Recognition Letters, Vol.26, No.5, pp.527-532, 2005.
  6. L. Wang, X. Wang, X. Zhang, and J. Feng "The equivalence of two-dimensional PCA to line-based PCA," Pattern Recognition Letters, Vol.26, No.1, pp.57-60, 2005.
  7. P. Nagabhushan, D. S. Guru, and B. H. Shekar, "(2D)2 FLD: An efficient approach for appearance based object recognition," Neurocomputing, Vol.69, No.7-9, pp.934-940, 2006.
  8. P. Samguansat, W. Asdornwised, S.Jitapunkul, and S. Marukatat. "Two dimensional linear discriminant analysis of principle component vectors for face recognition," IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), pp.345-348, 2006.
  9. S. Noushath, G. H. Kumar, and P. Shivakumara, "Diagonal Fisher linear discriminant analysis for efficient face recognition," Neurocomputing, Vol.69, No.13-15, pp.1711-1716, 2006.
  10. D. Zhang, Z. H. Zhou, and S. Chen, "Diagonal principal component analysis for face recognition," Pattern Recognition, Vol.39, No.1, pp.140-142, 2006.
  11. J. Yang, D. Zhang, X. Yong, and J. Y. Yang, "Two-dimensional discriminant transform for face recognition," Pattern Recognition, Vol.38, No.7, pp.1125-1129, 2005.
  12. L. Wang, X. Wang, and J. Feng, "On image matrix based feature extraction algorithms," IEEE Trans. Systems, Man and Cybernetics, Vol.36, No.1, pp.194-197, 2006.
  13. S. Noushath, G. H. and P. Shivakumara, "(2D)2LDA: An efficient approach for face recognition," Pattern Recognition, Vol.39, No.7, pp.1396-1400, 2006.
  14. Andy Hopper FREng, “The database of Faces,” AT&T Lab., “,” 2002.