Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace: http://hdl.handle.net/10609/1422

Título:	Multitask learning : An application to incremental face recognition
Autor/a:	Masip Rodo, David; Lapedriza Garcia, Àgata; Vitrià, Jordi
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 recognize. 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 together 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.
Materia(s):	-Educational technology -Human face recognition (Computer science) -Tecnologia educativa -Reconeixement facial (Informàtica) -Tecnología educativa -Reconocimiento facial (Informática)
Derechos:	NO
Tipo de documento:	Objeto de conferencia
Compartir:

Mostrar el registro completo del ítem