Automatic Facial Expression Recognition Using Extended AR-LBP

被引:0
|
作者
Shrinivasa, Naika C. L. [1 ]
Shekhar, Jha Shashi [1 ]
Pradip, Das K. [1 ]
Shivashankar, Nair B. [1 ]
机构
[1] Indian Inst Technol Guwahati, Gauhati, Assam, India
来源
WIRELESS NETWORKS AND COMPUTATIONAL INTELLIGENCE, ICIP 2012 | 2012年 / 292卷
关键词
AR-LBP; Facial Expression Recognition; Feature Extraction; Face Representation; Convolution; Extended AR-LB;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Local Binary Pattern (LBP) based operators are sensitive to localization errors. To mitigate these errors input images are manually aligned, face is localized using eyes co-ordinates in the image before feature extraction and multi-scale or multi operators are used, which restricts the use of LBP based operators for automatic facial expression recognition. This paper proposes an Extended Asymmetric Region Local Binary Pattern (EAR-LBP) operator and automatic face localization heuristics to mitigate the localization errors for automatic facial expression recognition. The proposed operator along with face localization heuristics was evaluated for person-independent facial expression recognition on JAFFE and CK+ databases using a multi-class SVM with Linear and Radial Basis Function (RBF) as kernels. It is observed that face localization and the EAR-LBP method are able to mitigate the localization errors to produce reasonably better performance. Maximum 10-fold cross validation average performance of 58.74% and 60.35% were obtained on JAFFE and in case of CK+ database, maximum performance of 83.09% and 82.21% were obtained using Linear and RBF kernels for SVM multi-class classifier respectively.
引用
收藏
页码:244 / 252
页数:9
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