Please use this identifier to cite or link to this item:
|Title:||Multitask Learning : an application to Incremental Face Recognition|
|Author:||Masip Rodó, David|
Lapedriza Garcia, Àgata
|Citation:||Masip, D.; Lapedriza, A.; Vitrià, J. (2008). "Multitask Learning : An application to Incremental Face Recognition". A: VISIGRAPP 2008, International joint conference on computer vision graphics theory and applications. INSTICC. Funchal. 21 - 25 de Gener.|
|Abstract:||Usually face classification applications suffer from two important problems: the number of training samples from each class is reduced, and the final system usually must be extended to incorporate new people to rec- ognize. In this paper we introduce a face recognition method that extends a previous boosting-based classifier adding new classes and avoiding the need of retraining the system each time a new person joins the system. The classifier is trained using the multitask learning principle and multiple verification tasks are trained to- gether sharing the same feature space. The new classes are added taking advantage of the previous learned structure, being the addition of new classes not computationally demanding. Our experiments with two differ- ent data sets show that the performance does not decrease drastically even when the number of classes of the base problem is multiplied by a factor of 8.|
|Appears in Collections:||Conference lectures|
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.