Two-Dimensional Weighted and Locality Preserved Discriminant Analysis for Face Recognition

Abstract:

In this paper, a novel method for face recognition, called two-dimensional weighted and locality preserved discriminant analysis (2D-WLPDA) is proposed. The new algorithm is developed based on three techniques: (1) locality preserved embedding, by embedding nearest-neighbor graphs which characterize the within-class compactness of the same class samples, 2D-WLPDA discovers the submanifold of images space; (2) image based projection which can avoids the small sample problem and improves the computation efficiency;(3) weighting contributions of individual class pairs which alleviates the overlap of neighboring classes in Fisher criterion for a k-class problem with k>2. We experimentally compare 2D-WLPDA to other feature extraction methods, such as 2D-LDA, 2D-PCA and 2D-DLPP, 2D-WLPDA has better recognition performance.

• Periodical: Applied Mechanics and Materials (Volumes 701-702)
• Pages: 418-423
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.701-702.418
• Online since: December 2014
• Authors: Yong Feng Qi*, Hong Wei Yang, Yuan Lian Huo
• Keywords: Face Recognition, Fisher Criterion, Locality Preserved Embedded, Weighted Function

References

[1] 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, 1997, 19(7): 711-720.

DOI: 10.1109/34.598228

[2] L. Chen, H. 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, 2000, 33(10): 1713–1726.

DOI: 10.1016/s0031-3203(99)00139-9

[3] D. Q. Dai and P. C. Yuen, Regularized discriminant analysis and its application to face recognition, Pattern Recognition, 2003, 36( 3): 845–847.

DOI: 10.1016/s0031-3203(02)00092-4

[4] I. Pima, M. Aladjem, Regularizedd is criminant analysis for face recognition, Pattern Recognition, 2004, 37( 9): 1945-(1948).

DOI: 10.1016/j.patcog.2004.03.011

[5] D. Q. Dai and P. C. Yuen, Face recognition by regularized discriminant analysis, IEEE Transactions on System, Man and Cybernetics-part B: Cybernetics, 37(4): 1080–1085.

DOI: 10.1109/tsmcb.2007.895363

[6] H. Li, T. Jiang and K. Zhang, Efficient and robust feature extraction by maximum margin criterion, IEEE transactions on neural networks, 2006, 17(1): 157-165.

DOI: 10.1109/tnn.2005.860852

[7] H. Cevikalp, M. Neamtu, M. Wilkes, A. Barkana, Discriminative common vectors for face recognition, IEEE Transactions on pattern analysis and machine intelligence . 2005, 27 (1): 4–13.

DOI: 10.1109/tpami.2005.9

[8] Li, M., Yuan, B., 2D-LDA: A statistical linear discriminant analysis for image matrix, Pattern Recognition Letters. 2005, 26 (5), 527–532.

DOI: 10.1016/j.patrec.2004.09.007

[9] W. Yu, Two-dimensional discriminant locality preserving projections for face recognition, Pattern Recognition Letters, 2009, 30(15): 1378-1383.

DOI: 10.1016/j.patrec.2009.07.004

[10] R. Zhi ,Q. Ruan, Two-dimensional direct and weighted linear discriminant analysis for face recognition, 2008, 71(16-18): 3607-3611.

DOI: 10.1016/j.neucom.2008.04.047

[11] R. P. W. Uuin and R. Haeb-Umbach, Multiclass linear dimension reduction by weighted pairwise fisher criteria, IEEE transactions on pattern analysis and machine intelligence, 2001, 23(7): 762-766.

DOI: 10.1109/34.935849

[12] Yang J., Zhang D., Frangi A.F., Yang J., Two dimensional PCA: A new approach to to appearance-based face representation and recognition , IEEE transactions on pattern analysis and machine intelligence, 2004, 26(1): 131-137.

DOI: 10.1109/tpami.2004.1261097