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
收藏
页码：1082 / 1087
页数：6
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