Facial Expression Recognition Using Neural Network Trained with Zernike Moments

被引：8

作者：

Saaidia, Mohammed ^{[1
]}

Zermi, Narima ^{[2
]}

Ramdani, Messaoud ^{[2
]}

机构：

[1] Univ MCM Souk Ahras, Dept Genie Elect, Souk Ahras, Algeria

[2] Univ BM Annaba, Dept Elect, Annaba, Algeria

来源：

PROCEEDINGS 2014 4TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE WITH APPLICATIONS IN ENGINEERING AND TECHNOLOGY ICAIET 2014 | 2014年

关键词：

face detection; face expression recognition; image analysis; patern recognition; FEATURE-EXTRACTION; FACE;

D O I：

10.1109/ICAIET.2014.39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Neural network classifying method is used in this work to perform facial expression recognition. The processed expressions were the six most pertinent facial expressions and the neutral one. This operation was implemented in three steps. First, a neural network, trained using Zernike moments, was applied to the set of the well known Yale and JAFFE database images to perform face detection. In the second step, detected faces are processed to perform the characterization phase through computed vectors of Zernike moments. At last step, a back propagation neural network was trained to distinguish between the seven emotion's states of a presented face. Finally, method performances were evaluated on the well known JAFEE and YALE database.

引用

页码：187 / 192

页数：6

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