Model Representation for Facial Expression Recognition Based on Shape and Texture

Author

Cruz de Gois, Adriana ; Antonino, V.O. ; Tsang Ing Ren ; Cavalcanti, G.D.C.

Author_Institution

Center for Inf. (CIn), Fed. Univ. of Pernambuco (UFPE), Recife, Brazil

Volume

1

fYear

2012

fDate

7-9 Nov. 2012

Firstpage

1082

Lastpage

1087

Abstract

In this paper, we present an efficient method for facial expression recognition. Three features extraction methods are combined to form a model representation for facial expressions. Once the feature and the model representation are defined a Support Vector Machine (SVM) is used for the classification task. The proposed method is tested using the Yale and Cohn-Kanade databases, which contains 165 images and 1480 images, respectively. The method presented a recognition rate of 98.1% and 93% for the Yale and Cohn-Kanade respectively.

Keywords

face recognition; image classification; image representation; image texture; shape recognition; support vector machines; Cohn-Kanade databases; SVM; Yale databases; classification task; facial expression recognition; model representation; shape; support vector machine; texture; Databases; Equations; Hidden Markov models; Image edge detection; Mathematical model; Mouth; Training; Elastic shape-texture matching; Facial expression recognition; Gabor Filter; Local Binary Pattern; Spatially Maximum occurrence model; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on

Conference_Location

Athens

ISSN

1082-3409

Print_ISBN

978-1-4799-0227-9

Type

conf

DOI

10.1109/ICTAI.2012.153

Filename

6495170