Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets.
Neural Netw
; 24(8): 814-23, 2011 Oct.
Article
em En
| MEDLINE
| ID: mdl-21820862
ABSTRACT
In this paper, the robustness of appearance-based subspace learning techniques in geometrical transformations of the images is explored. A number of such techniques are presented and tested using four facial expression databases. A strong correlation between the recognition accuracy and the image registration error has been observed. Although it is common-knowledge that appearance-based methods are sensitive to image registration errors, there is no systematic experiment reported in the literature. As a result of these experiments, the training set enrichment with translated, scaled and rotated images is proposed for confronting the low robustness of these techniques in facial expression recognition. Moreover, person dependent training is proven to be much more accurate for facial expression recognition than generic learning.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Reconhecimento Psicológico
/
Expressão Facial
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Neural Netw
Ano de publicação:
2011
Tipo de documento:
Article