An alternative formulation of kernel LPP with application to image recognition

Neurocomputing

Volumn 69, Issue 13-15, 2006, Pages 1733-1738

  Feng, Guiyu ^a   Hu, Dewen ^a   Zhang, David ^b   Zhou, Zongtan ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Image recognition; Kernel LPP (KLPP); Kernel principal component analysis (KPCA); Locality preserving projections (LPP)

Indexed keywords

ALGORITHMS; COMPUTATIONAL GEOMETRY; FEATURE EXTRACTION; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS;

IMAGE RECOGNITION; LOCALITY PRESERVING PROJECTIONS;

IMAGE PROCESSING;

ALGORITHM; ARTICLE; CONTROLLED STUDY; DATA BASE; FACE; GEOMETRY; HUMAN; HUMAN EXPERIMENT; IMAGE ANALYSIS; METHODOLOGY; NONLINEAR SYSTEM; NORMAL HUMAN; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; STATISTICAL ANALYSIS; STATISTICAL MODEL; STRUCTURE ANALYSIS;

EID: 33745188262     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.01.006     Document Type: Article

References (16)

1. Belhumeur P., Hespanha J., and Kriegman D. Eigenfaces vs. fisherfaces: recognition using class specific linear projection,. IEEE Trans. Pattern Anal. Mach. Intell. 19 7 (2001) 711-720
2. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, In: Proceedings of Advance in Neural Information Processing System 14, Vancouver, Canada, December 2001, pp. 585-591.
3. X. He, P. Niyogi, Locality preserving projections, In: Proceedings of Advance in Neural Information Processing System 16, Vancouver, Canada, December 2003, Available at: http://books.nips.cc/papers/files/nips16/NIPS2003_AA20.pdf
4. He X., Yan S., Hu Y., Niyogi P., and Zhang H.-J. Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27 3 (2005) 328-340
5. X. He, S. Yan, Y. Hu, H. -J. Zhang, Learning a locality preserving subspace for visual recognition, In: Proceedings of International Conference on Computer Vision, Rice, France, October 2003, pp. 385-392.
6. Lu G., Zhang D., and Wang K. Palmprint recognition using eigenpalms features. Pattern Recognition Lett. 24 9-10 (2003) 1463-1467
7. Schölkopf B., Smola A., and Muller K.R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10 5 (1998) 1299-1319
8. The ORL face database, Available at: http://www.cam_orl.co.uk/facedatabase.html.
9. The PolyU palmprint database, Available at: http://www.comp.polyu.edu.hk/biometrics/
10. Turk M., and Pentland A. Eigenfaces for recognition. J. Cogn. Neurosci. 3 1 (1991) 71-86
11. Wu X., Zhang D., and Wang K. Fisherpalms based palmprint recognition. Pattern Recognition Lett. 24 15 (2003) 2829-2838
12. Yang J., Frangi A.F., and Yang J.Y. A new kernel fisher discriminant algorithm with application to face recognition. Neurocomputing 56 (2004) 415-421
13. Yang J., Frangi A.F., Yang J.Y., Zhang D., and Jin Z. Kernel PCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and representation. IEEE Trans. Pattern Anal. Mach. Intell. 27 2 (2005) 230-244
14. Yang J., Gao X., Zhang D., and Yang J.Y. Kernel ICA: an alternative formulation and its application to face recognition. Pattern Recognition 38 10 (2005) 1784-1787
15. M.H. Yang, Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods, In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington DC, USA, May 2002, pp. 215-220.
16. Zhang D., Kong W.K., You J., and Wong M. Online palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 25 9 (2003) 1041-1050