**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**32797

##### Bidirectional Discriminant Supervised Locality Preserving Projection for Face Recognition

**Abstract:**

**Keywords:**
Face recognition,
dimension reduction,
locality
preserving projection,
discriminant information,
bidirectional
projection.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.3593178

**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.