Weighted Sub-Gabor for face recognition

Pattern Recognition Letters

Volumn 28, Issue 4, 2007, Pages 487-492

  Nanni, Loris ^a   Maio, Dario ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

Classification; Face recognition; Machine learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE RETRIEVAL; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; VECTORS;

GABOR FILTERS; KARHUNEN-LOEVE TRANSFORM; PARZEN WINDOW CLASSIFIERS;

FACE RECOGNITION;

EID: 33845473088     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2006.09.002     Document Type: Article

References (23)

1. Bartlett M.S., Movellan J.R., and Sejnowski T.J. Face recognition by independent component analysis. IEEE Trans. Neural Networks 13 6 (2002) 1450-1464
2. Cappelli, R., Maio, D., Maltoni, D. 2002. Subspace classification for face recognition. In: Proc. Workshop on Biometric Authentication - ECCV'02 (BIOW2002), Copenhagen, pp. 133-141.
3. Duda R.O., Hart P.E., and Stork D. Pattern Classification. second ed. (2000), Wiley
4. Fortuna J., and Capson D. Improved support vector classification using PCA and ICA feature space modification. Pattern Recognition 37 (2004) 1117-1129
5. Goldberg D. Genetic Algorithms in Search, Optimization, and Machine Learning (1989), Addison-Wesley
6. Gottumukkal R., and Asari V.K. An improved face recognition technique based on modular PCA approach. Pattern Recognition Lett. 25 4 (2004) 429-436
7. Heisele B., Ho P., Wu J., and Poggio T. Face recognition: Comparing component-based and global approaches. Computer Vision and Image Understanding 91 1-2 (2003) 6-21
8. Kim, T.-K., Kim, H., Hwang, W., Kee, S., Lee, J. 2002. Component-based LDA face descriptor for image retrieval. IAPR British Machine Vision Conference (BMVC), Cardiff, UK, September 2-5.
9. Krame M. Nonlinear principal component analysis using auto-associative neural networks. AIChE J. 37 (1991) 233-243
10. Kuncheva L.I. Combining Pattern Classifiers: Methods and Algorithms (2004), Wiley July
11. Liu C. Gabor-based Kernel PCA with fractional power polynomial models for face recognition. IEEE PAMI 26 5 (2004)
12. Manjunath, B., Chellappa, R., von der Malsburg, C. 1992. A feature based approach to face recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 373-378.
13. Phillips P.J., Wechsler H., Huang J., and Rauss P. The FERET database and evaluation procedure for face recognition algorithms. Image Vision Comput. J. 16 5 (1998) 295-306
14. Phillips P.J., Moon H., Rizvi S.A., and Rauss P.J. The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Machine Intell. 22 (2000) 1090-1104
15. Tan K., and Chen S. Adaptively weighted sub-pattern PCA for face recognition. Neurocomputing 64 (2005) 505-511
16. Tefas A., Kotropoulos C., and Pitas I. Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans. Pattern Anal. Machine Intell. 23 7 (2001) 735-746
17. Turk M., and Pentland A. Eigenfaces for recognition. J. Cognitive Neurosci. (1991) 71-86
18. Wiskott L., Fellous J.M., Kruger N., and von der Malsburg C. Face recognition by elastic bunch graph matching. IEEE Trans. PAMI 19 7 (1997) 775-779
19. Yang M., Ahuja N., and Kriegman D. Face recognition using kernel eigenfaces. Proc. Internat. Conf. Image Process. (2000) 37-40
20. Yang, P., Shan, S., Gao, W., Li, S.Z., Zhang, D. 2004. Face recognition using Ada-boosted Gabor features. In: 6th IEEE Internat. Conf. on Automatic Face and Gesture Recognition, p. 356.
21. Zana, Y., Cesar, J.R., Roberto, M. Face recognition based on polar frequency features. Available from: .
22. Zhao, W., Chellappa, R., Krishnaswamy, A. 1998. Discriminant analysis of principal components for face recognition. In: Proc. Internat. Conf. on Face and Gesture Recognition.
23. Zhao, W. et al. 2000. Face recognition: A literature survey, Tech. Rep. Univ. of Maryland.