Bidirectional Discriminant Supervised Locality Preserving Projection for Face Recognition

Dimensionality reduction and feature extraction are of
crucial importance for achieving high efficiency in manipulating
the high dimensional data. Two-dimensional discriminant locality
preserving projection (2D-DLPP) and two-dimensional discriminant
supervised LPP (2D-DSLPP) are two effective two-dimensional
projection methods for dimensionality reduction and feature
extraction of face image matrices. Since 2D-DLPP and 2D-DSLPP
preserve the local structure information of the original data and
exploit the discriminant information, they usually have good
recognition performance. However, 2D-DLPP and 2D-DSLPP
only employ single-sided projection, and thus the generated low
dimensional data matrices have still many features. In this paper,
by combining the discriminant supervised LPP with the bidirectional
projection, we propose the bidirectional discriminant supervised LPP
(BDSLPP). The left and right projection matrices for BDSLPP can
be computed iteratively. Experimental results show that the proposed
BDSLPP achieves higher recognition accuracy than 2D-DLPP,
2D-DSLPP, and bidirectional discriminant LPP (BDLPP).

Authors:



References:
[1] P. Baldi and G. W. Hatfield, DNA Microarrays and Gene Expression:
From Experiments to Data Analysis and Modeling, Cambridge, 2002.
[2] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs.
Fisherfaces: Recognition using class specific linear projection, IEEE
Transactions on Pattern Analysis and Machine Intelligence, 19 (1997),
pp. 711–720.
[3] C. M. Bishop, Pattern Recognition and Machine Learning, Springer,
2006.
[4] L. Chen, H. M. Liao, M. Ko, J. Lin, and G. Yu, A new LDA-based
face recognition system which can solve the small sample size problem,
Pattern Recognition, 33 (2000), pp. 1713–1726.
[5] S. B. Chen, H. F. Zhao, M. Kong, and B. Luo, 2D-LPP:
a two-dimensional extension of locality preserving projections,
Neurocomputing, 70 (2007), pp. 912–921.
[6] W. K. Ching, D. Chu, L. Z. Liao, and X. Wang, Regularized
orthogonal linear discriminant analysis, Pattern Recognition, 45 (2012),
pp. 2719–2732.
[7] D. Chu and S. T. Goh, A new and fast implementation for null space
based linear discriminant analysis, Pattern Recognition, 43 (2010),
pp. 1373–1379.
[8] , A new and fast orthogonal linear discriminant analysis on
undersampled problems, SIAM Journal on Scientific Computing, 32
(2010), pp. 2274–2297.
[9] D. Chu, S. T. Goh, and Y. S. Hung, Characterization of all solutions
for undersampled uncorrelated linear discriminant analysis problems,
SIAM Journal on Matrix Analysis and Applications, 32 (2011),
pp. 820–844.
[10] D. Q. Dai and P. C. Yuen, Regularized discriminant analysis and
its application to face recognition, Pattern Recognition, 36 (2003),
pp. 845–847.
[11] R. Q. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, John
Wiley and Sons, second ed., 2001.
[12] S. Dudoit, J. Fridlyand, and T. P. Speed, Comparison of discrimination
methods for the classification of tumors using gene expression data,
Journal of the American Statistical Association, 97 (2002), pp. 77–87.
[13] J. H. Friedman, Regularized discriminant analysis, Journal of the
American Statistical Association, 84 (1989), pp. 165–175.
[14] K. Fukunaga, Introduction to Statistical Pattern Recognition, CA:
Academic, San Diego, second ed., 1990.
[15] Y. Guo, T. Hastie, and R. Tibshirani, Regularized linear discriminant
analysis and its application in microarray, Biostatistics, 8 (2007),
pp. 86–100.
[16] X. He and P. Niyogi, Locality preserving projections, Advances in
Neural Information Processing Systems, 16 (2004), pp. 153–160.
[17] , Tensor subspace analysis, Advances in Neural Information
Processing Systems, 18 (2005).
[18] P. Howland and H. Park, Generalizing discriminant analysis using the
generalized singular value decomposition, IEEE Transactions on Pattern
Analysis and Machine Intelligence, 26 (2004), pp. 995–1006.
[19] A. K. JAIN AND R. C. DUBES, Algorithms for Clustering Data, Prentice
Hall, 1988.
[20] Z. Jin, J. Y. Yang, Z. S. Hu, and Z. Lou, Face recognition based on
the uncorrelated discriminant transformation, Pattern Recognition, 34
(2001), pp. 1405–1416.
[21] G. Kowalski, Information Retrieval Systems: Theory and
Implementation, Kluwer Academic Publishers, 1997. [22] M. Li and B. Z. Yuan, 2D-LDA: a statistical linear discriminant analysis
for image matrix, Pattern Recognition Letters, 26 (2005), pp. 527–532.
[23] C. X. Ren and D. Q. Dai, Bilinear lanczos components for fast
dimensionality reduction and feature extraction, Pattern Recognition, 43
(2010), pp. 3742–3752.
[24] D. L. Swets and J. Weng, Using discriminant eigenfeatures for
image retrieval, IEEE Transactions on Pattern Analysis and Machine
Intelligence, 18 (1996), pp. 831–836.
[25] M. Turk and A. Pentland, Eigenfaces for recognition, Journal of
Cognitive Neuroscience, 3 (1991), pp. 71–86.
[26] S. J. Wang, C. G. Zhou, N. Zhang, X. J. Peng, Y. H. Chen, and X. Liu,
Face recognition using second-order discriminant tensor subspace
analysis, Neurocomputing, 74 (2011), pp. 2142–2156.
[27] Y. Xu, G. Feng, and Y. Zhao, One improvement to two-dimensional
locality preserving projection method for use with face recognition,
Neurocomputing, 73 (2009), pp. 245–249.
[28] J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, Two-dimensional PCA: a
new approach to appearance-based face representation and recognition,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 26
(2004), pp. 131–137.
[29] J. Ye, Characterization of a family of algorithms for generalized
discriminant analysis on undersampled problems, Journal of Machine
Learning Research, 6 (2005), pp. 483–502.
[30] , Generalized low rank approximations of matrices, Machine
Learning, 61 (2005), pp. 167–191.
[31] J. Ye, R. Janardan, C. H. Park, and H. Park, An optimization criterion
for generalized discriminant analysis on undersampled problems, IEEE
Transactions on Pattern Analysis and Machine Intelligence, 26 (2004),
pp. 982–994.
[32] J. Ye and Q. Li, A two-stage linear discriminant analysis via
QR-decomposition, IEEE Transactions on Pattern Analysis and Machine
Intelligence, 27 (2005), pp. 929–941.
[33] W. Yu, Two-dimensional discriminant locality preserving projections for
face recognition, Pattern Recognition Letters, 30 (2009), pp. 1378–1383.
[34] W. Yu, X. Teng, and C. Liu, Face recognition using discriminant
locality preserving projections, Image and Vision Computing, 24 (2006),
pp. 239–248.
[35] Z. Zheng, F. Yang, and W. Tan, Gabor feature-based face recognition
using supervised locality preserving projections, Signal Processing, 87
(2007), pp. 2473–2483.
[36] L. Zhu and S. Zhu, Face recognition based on orthogonal
discriminant locality preserving projections, Neurocomputing, 70
(2007), pp. 1543–1546.