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Discriminant analysis of the two-dimensional Gabor features for face recognition

Discriminant analysis of the two-dimensional Gabor features for face recognition

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A new technique called two-dimensional Gabor Fisher discriminant (2DGFD) is derived and implemented for image representation and recognition. In this approach, the Gabor wavelets are used to extract facial features. The principal component analysis (PCA) is applied directly on the Gabor transformed matrices to remove redundant information from the image rows and a new direct two-dimensional Fisher linear discriminant (direct 2DFLD) method is derived in order to further remove redundant information and form a discriminant representation more suitable for face recognition. The conventional Gabor-based methods transform the Gabor images into a high-dimensional feature vector. However, these methods lead to high computational complexity and memory requirements. Furthermore, it is difficult to analyse such high-dimensional data accurately. The novel 2DGFD method was tested on face recognition using the ORL, Yale and extended Yale databases, where the images vary in illumination, expression, pose and scale. In particular, the 2DGFD method achieves 98.0% face recognition accuracy when using 20×3 feature matrices for each Gabor output on the ORL database and 97.6% recognition accuracy compared with 91.8% and 91.6% for the 2DPCA and 2DFLD method on the extended Yale database. The results show that the proposed 2DGFD method is computationally more efficient than the Gabor Fisher classifier method by approximately 8 times on the ORL, 135 times on the Yale and 1.2801×108 times on the extended Yale B data sets.

References

    1. 1)
      • G.H. Golub , C.F. Van Loan . (1989) Matrix computations.
    2. 2)
      • Chung, K.-C., Kee, S.C., Kim, S.R.: `Face recognition using principal component analysis of Gabor filter responses', Proc. Int. Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 1999, p. 53–57.
    3. 3)
      • I. Dagher , R. Nachar . Face recognition using IPCA-ICA algorithm. IEEE Trans. Pattern Anal. Mach. Intell. , 996 - 1000
    4. 4)
      • Cho, D.U., Chang, U.D., Kim, B.H., Lee, S.H., Bae, Y.L.J., Ha, S.C.: `2D direct LDA algorithm for face recognition', Proc. 4th Int. Conf. Software Engineering Research, Management and Applications, 2006, p. 245–248.
    5. 5)
      • X. He , S. Yan , Y. Hu , P. Niyogi , H.-J. Zhang . Face recognition using Laplacian faces. IEEE Trans. Pattern Anal. Mach. Intell. , 328 - 340
    6. 6)
      • ‘The Olivetti Research Laboratory (ORL) database,’ 1994.
    7. 7)
      • J. Yang , D. Zhang , A.F. Frangi , J.-Y. Yang . Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. , 131 - 137
    8. 8)
      • N. Vaswani , R. Chellappa . Principal components null space analysis for image and video classification. IEEE Trans. Image Process. , 1816 - 1830
    9. 9)
      • M. Li , B. Yuan . 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognit. Lett. , 527 - 532
    10. 10)
      • X. Xie , K. Lam . Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image. IEEE Trans. Image Process. , 2481 - 2492
    11. 11)
      • R.M. Mutelo , L.C. Khor , W.L. Woo , S.S. Dlay . Two-dimensional reduction PCA: a novel approach for feature extraction, representation, and recognition. Proc. SPIE
    12. 12)
      • M.C. Morrone , D.C. Burr . Feature detection in human vision: A phase dependent energy model. Proc. R. Soc. London , 221 - 245
    13. 13)
      • J. Yang , J.-Y. Yang . From image vector to matrix: a straightforward image projection technique–IMPCA vs. PCA. Pattern Recognit. , 1997 - 1999
    14. 14)
      • H. Yu , J. Yang . A direct LDA algorithm for high-dimensional data – with application to face recognition. Pattern Recognit. , 2067 - 2070
    15. 15)
      • S. Edelman . (1999) Representation and Recognition in vision.
    16. 16)
      • H. Xiong , M.N.S. Swamy , M.O. Ahmad . Two-dimensional FLD for face recognition. Pattern Recognit. , 1121 - 1124
    17. 17)
      • R.O. Duda , P.E. Hart . (1973) Pattern classification and scene analysis.
    18. 18)
      • A.S. Georghiades , D.J. Kriegman , P.N. Belhumeur . From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. , 643 - 660
    19. 19)
      • P.N. Belhumeur , J.P. Hespanha , D.J. Kriegman . Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. , 711 - 720
    20. 20)
      • M. Kirby , M. Sirovich . Application of the Karhunen–Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. , 103 - 108
    21. 21)
      • G. Donato , M.S. Bartlett , J.C. Hager , P. Ekman , T.J. Sejnowski . Classifying facial actions. IEEE Trans. Pattern Anal. Mach. Intell. , 974 - 989
    22. 22)
      • M. Lades , J.C. Vorbrüggen , J. Buhmann , J. Lange , C. von der Malsburg , R.P. Würtz , W. Konen . Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. , 300 - 311
    23. 23)
      • L.-F. Chen , H.-Y.M. Liao , M.-T. Ko , J.-C. Lin , G.-J. Yu . A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognit. , 1713 - 1726
    24. 24)
      • M. Turk , A. Pentland . Eigenfaces for recognition. J. Cogn. Neurosc. , 71 - 86
    25. 25)
      • C. Liu , H. Wechsler . Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. , 467 - 476
    26. 26)
      • D.J. Field . Relation between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. , 2379 - 2394
    27. 27)
      • K.-C. Lee , J. Ho , D. Kriegman . Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. , 684 - 698
    28. 28)
      • Xiang, C., Fan, X.A., Lee, T.H.: `Face recognition using recursive Fisher linear discriminant with Gabor wavelet coding', International Conf. Image Processing (ICIP ′04), 2004.
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