Multiclass mv-granular soft support vector machine: A case study in dynamic classifier selection for multispectral face recognition

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2008, Pages

  Singh, Richa ^a   Vatsa, Mayank ^a   Noore, Afzel ^a  

^a WEST VIRGINIA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GRANULAR COMPUTING; IMAGE FUSION; INFRARED RADIATION; SUPPORT VECTOR MACHINES;

COMPUTATIONAL TIME; DATA DISTRIBUTION; DYNAMIC CLASSIFIER SELECTION; DYNAMIC SELECTION; MULTI-CLASS CLASSIFIER; MULTI-CLASS SUPPORT VECTOR MACHINES; SHORT WAVE INFRARED; VISIBLE SPECTRA;

FACE RECOGNITION;

EID: 71749107922     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icpr.2008.4761877     Document Type: Conference Paper

References (11)

1. A. Bargiela and W. Pedrycz. Granular computing: an introduction. International Series in Engineering and Computer Science, 2002.
2. H. G. Chew, C. C. Lim, and R. E. Bogner. An implementation of training dual-ν support vector machines. Optimization and Control with Applications, Qi, Teo, and Yang (Editors), 2004.
3. C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2):415-425, 2002.
4. A. Ross, K. Nandakumar, and A. K. Jain. Handbook of multibiometrics. Springer, New York, 1st edition, 2006.
5. R. Singh, M. Vatsa, and A. Noore. Face recognition with disguise and single gallery images. Image and Vision Computing, In Press 2008.
6. R. Singh, M. Vatsa, and A. Noore. Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition. Pattern Recognition, 41(3):880-893, 2008.
7. Posterior probability support vector machines for unbalanced data
8. V. Vapnik. The nature of statistical learning theory. Springer, 1995.
9. M. Vatsa, R. Singh, and A. Noore. Unification of evidence theoretic fusion algorithms: A case study in level- 2 and level-3 fingerprint features. In Proceedings of IEEE International Conference on Biometrics: Theory, Applications, and Systems, pages 1-6, 2007.
10. M. Vatsa, R. Singh, and A. Noore. Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory. International Journal of Approximate Reasoning, In Press 2008.
11. Z. Zivkovic and F. Heijden. Recursive unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):651- 656, 2004.