Sunday, 20 de April de 2014

Ficha del recurso:

Fuente:

Vínculo original en 2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011
Nikitidis, S; Tefas, A; Nikolaidis, N; Pitas, I

Última actualización:

Thursday, 29 de March de 2012

Entrada en el observatorio:

Thursday, 29 de March de 2012

Idioma:

Inglés

Archivado en:


FACIAL EXPRESSION RECOGNITION USING CLUSTERING DISCRIMINANT NON-NEGATIVE MATRIX FACTORIZATION

Non-negative Matrix Factorization (NMF) is among the most popular subspace methods widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis criteria and achieves an efficient decomposition of the provided data to its discriminant parts has been proposed. However, this approach poses several limitations since it assumes that the underline data distribution forms compact sets which is often unrealistic. To remedy this limitation we regard that data inside each class form various number of clusters and apply a Clustering based Discriminant Analysis. The proposed method combines appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space. Experimental results performed on the Cohn-Kanade database verified the effectiveness of the proposed met! hod in the facial expression recognition task.