Regularized locality preserving discriminant embedding for face recognition

Neurocomputing

Volumn 77, Issue 1, 2012, Pages 156-166

  Pang, Ying Han ^a   Andrew Beng, Jin Teoh ^b,c   Abas, Fazly Salleh ^a  

^a MULTIMEDIA UNIVERSITY   (Malaysia)

^b Yonsei University   (South Korea)

^c SUNWAY UNIVERSITY   (Malaysia)

Author keywords

Adaptive locality preserving regulation model; Class discrimination; Face recognition; Graph embedding; Locality preserving capability

Indexed keywords

BIASED ESTIMATION; CLASS DISCRIMINATION; DATA LOCALITY; DATA SETS; DISCRIMINANT EMBEDDING; GRAPH EMBEDDINGS; LINEAR GRAPH; LOCALITY PRESERVING CAPABILITY; LOCALITY PRESERVING FUNCTIONS; OVER FITTING PROBLEM; POPULATION DATA; RECOGNITION PERFORMANCE; REGULATION MODELS; TRAINING SAMPLE;

POPULATION STATISTICS;

FACE RECOGNITION;

ARTICLE; DISCRIMINANT ANALYSIS; ERROR; FACE; FACE RECOGNITION; FISHER EXACT TEST; HUMAN; IMAGE ANALYSIS; KERNEL METHOD; MULTIPLE REGRESSION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RECOGNITION; REGULARIZED LOCALITY PRESERVING DIDCRIMINANT EMBEDDING; VISUAL MEMORY;

EID: 80955134088     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.09.007     Document Type: Article

References (30)

1. Martinez A.M., Kak A.C. PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23(2):228-233.
2. Joshua B.T., Vin d.S., John C.L. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290(22):2319-2323.
3. Cai D., He X., Han J., Zhang H. Orthogonal Laplacianfaces for face recognition. IEEE Trans. Image Process. 2006, 15(11):3608-3614.
4. D. Cai, X. He, K. Zhou, J. Han, H. Bao, Locality sensitive discriminant analysis, in: Proceedings of the International Joint Conference Artificial Intelligence, 2007, pp. 708-713.
5. D. Cai, X. He, Y. Hu, J. Han, H. Thomas, Learning a spatially smooth subspace for face recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-7.
6. F. Chung, Spectral graph theory, in: Proceedings of the Regional Conference Series in Mathematis, no. 92, 1997.
7. H. Chen, H.W. Chang, L. Liu, Local discriminant embedding and its variants, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 846-853.
8. Lu J., Tan Y.P. Regularized locality preserving projections and its extensions for face recognition. IEEE Trans. Syst. Man. Cybern. 2010, 40(3):958-963.
9. Fukunnaga K. Introduction to Statistical Pattern Recognition 1991, Academic Press. second ed.
10. Saul L., Roweis S.T. An Introduction to Locally Linear Embedding 2000, Report at AT&T labs-Research. http://www.cs.toronto.edu/roweis/lle/publications.html.
11. Saul L., Roweis S.T. Think globally, fit locally: unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res. 2003, 4:119-155.
12. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Proceedings of the Conference on Advances in Neural Information Processing System 15, 2001.
13. Bichsel M., Pentland A. Human face recognition and the face image set's topology. CVGIP: Image Understan. 1994, 59(2):254-261.
14. Keil M.S., Lapedriza Agata, Masip David, Vitria Jordi Preferred spatial frequencires for human face processing are associated with optimal class discrimination in the machine. PLoS ONE 2008, 3(7):e2590.
15. Turk M., Pentland A. Eigenfaces for recognition. J. Cognitive Neurosci. 1991, 3(1):71-86.
16. Han Pang Ying, Andrew Teoh B.J., Abas Fazly Salleh Neighbourhood preserving discriminant embedding in face recognition. J. Vis. Commun. Image R. 2009, 20(8):532-542.
17. Phillips P.J., Moon H., Rauss P.J., Rizvi S. The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. October 2000, 22(10):1090-1104.
18. P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, W. Worek, Overview of the face recognition grand challenge, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, CVPR05, 2005, pp. 947-954.
19. Belhumeur P.N., Hespanha J.P., Kriegman D.J., Eigenfaces vs Fisherfaces: recognition using class specific linear. IEEE Trans. Pattern Anal. Mach. Intell. 1997, 19:711-720.
20. Yan S.C., Xu Dong, Zhang Benyu, Zhang Hong-Jiang, Yang Qiang, Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(1):40-51.
21. Roweis S.T., Saul L. Nonlinear dimensionality reduction by locally linearc embedding. Science 2000, 290(5500):2323-2326.
22. Stan Z. Li, Rong Xiao, ZeYu Li, HongJiang Zhang, Nonlinear mapping from multi-view face patterns to a gaussian distribution in a low dimensional space, in: Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001, pp. 47-54.
23. D.M.J. Tax, Regularizing the covariance matrix using spatial information, in: Proceedings of the Sixteenth Belgium-Netherlands Conference on Artificial Intelligence, 2004, pp. 179-186.
24. Sim T., Baker S., Bsat M. The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25(12):1615-1618.
25. Dong X., Lin S., Yan S.C., Tang X. Rank-one projections with adaptive margins for face recognition. IEEE Trans. Syst. Man Cybern. 2007, 37(5):1226-1236.
26. Jiang X., Bappaditya M., Kot A. Eigenfeature regularization and extraction in face recogntion. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30(3):383-394.
27. X. He, Deng Cai, S. Yan, H.J. Zhang, Neighborhood preserving embedding, in: Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, pp. 1208-1213.
28. He X., Niyogi P. Locality preserving projections. Advances in Neural Information Processing Systems 2003, vol. 16. MIT Press, Cambridge, MA.
29. He X., Yan S., Hu Y., Niyogi P., Zhang H. Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(3):328-340.
30. Fu Y., Yan S., Huang T.S. Classification and feature extraction by simplexization. IEEE Trans. Inf. Forensics Secur. 2008, 3(1):91-100.