Model Representation for Facial Expression Recognition Based on Shape and Texture

被引：0

作者：

de Gois, Adriana Cruz ^{[1
]}

Antonino, Victor Oliveria ^{[1
]}

Ren, Tsang Ing ^{[1
]}

Cavalcanti, George D. C. ^{[1
]}

机构：

[1] Univ Fed Pernambuco, CIn, Recife, PE, Brazil

来源：

2012 IEEE 24TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2012), VOL 1 | 2012年

关键词：

Facial expression recognition; Local Binary Pattern; Spatially Maximum occurrence model; Elastic shape-texture matching; Gabor Filter; Support Vector Machine; FEATURE-EXTRACTION; IMAGE SEQUENCES; FACE;

D O I：

10.1109/ICTAI.2012.153

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1082 / 1087

页数：6

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