Title :
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
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;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on
Conference_Location :
Athens
Print_ISBN :
978-1-4799-0227-9
DOI :
10.1109/ICTAI.2012.153
