Multitask learning - An application to incremental face recognition

VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings

Volumn 1, Issue , 2008, Pages 585-590

  Masip, David ^a   Lapedriza, Ágata ^b   Vitrià, Jordi ^c  

^a UNIVERSITAT OBERTA DE CATALUNYA   (Spain)

^b UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

^c UNIVERSITY OF BARCELONA   (Spain)

Author keywords

Boosting; Face classification; Incremental learning

Indexed keywords

APPLICATIONS; CLASSIFIERS; COMPUTER VISION; EDUCATION; IMAGE PROCESSING; LEARNING SYSTEMS;

BOOSTING; DATA SETS; FACE CLASSIFICATION; FACE RECOGNITION METHODS; FEATURE SPACES; INCREMENTAL LEARNING; MULTI-TASK LEARNINGS; NEW CLASSES; TRAINING SAMPLES; VERIFICATION TASKS;

FACE RECOGNITION;

EID: 57349108060     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Baxter, J. (2000). A model of inductive bias learning. Journal of Machine Learning Research, 12:149-198.
2. Bressan, M. and Virria, J. (2003). Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recognition Letters, 24(15):2743-2749.
3. Caruana, R. (1997). Multitask learning. Machine Learning, 28(1):41-75.
4. Fisher, R. (1936). The use of multiple measurements in taxonomie problems. Ann. Eugenics, 7:179-188.
5. Freund, Y. and Schapire, R. E. (1996). Experiments with a new boosting algorithm. In International Conference on Machine Learning, pages 148-156.
6. Friedman, J., T.Hastie, and R.Tibshirani (2000). Additive logistic regression: a statistical view of boosting. Annals of statistics, 28:337-374.
7. Fukunaga, K. and Mantock, J. (1983). Nonparametric discriminant analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(6):671-678.
8. Ji Zhu, Saharon Rosset, H. Z. and Hastie, T. (2006). Multiclass adaboost. Technical report, Standford University.
9. Martinez, A. and Benavente, R. (1998). The AR Face database. Technical Report 24, Computer Vision Center.
10. Masip, D., Kuncheva, L. I., and Vitria, J. (2005). An ensemble-based method for linear feature extraction for two-class problems. Pattern Analysis and Applications, 8:227-237.
11. Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., and Worek, W. (2005). The 2005 IEEE workshop on face recognition grand challenge experiments. In CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05) - Workshops, page 45, Washington, DC, USA. IEEE Computer Society.
12. Schapire, R. E. (1997). Using output codes to boost multiclass learning problems. In Proc. 14th International Conference on Machine Learning, pages 313-321. Morgan Kaufmann.
13. Torralba, A., Murphy, K., and Freeman, W. (2004). Sharing features: efficient boosting procedures for multiclass object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
14. Turk, M. and Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1): 71-86.
15. Yokono, J. J. and Poggio, T. (2006). A multiview face identification model with no geometric constraints. Technical report, Sony Intelligence Dynamics Laboratories.