Facial expression recognition using ELBP based on covariance matrix transform in KLT

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• 作者：Min Guo ; Xiaohong Hou ; Yuting Ma ; Xiaojun Wu
• 关键词：Facial expression recognition ; Extended local binary pattern ; Covariance matrix transform ; KLT ; Machine learning
• 刊名：Multimedia Tools and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：2
• 页码：2995-3010
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
• 出版者：Springer US
• ISSN：1573-7721
• 卷排序：76

文摘

According to the deficiencies of Local Binary Pattern (LBP), the dimension of extraction is large, and it is not conducive to describe all characteristics of image texture, this paper proposes a novel facial expression recognition algorithm “K-ELBP” which uses uniform patterns of Extended Local Binary Pattern (ELBP), and combines with the covariance matrix transform in K-L transform (KLT). In this paper, ELBP is used for the first step to extract the feature matrix of expression images, then covariance matrix transform is applied to the ELBP matrix for reducing the dimension, which aims at extracting the main feature vectors. And the best recognition performance is obtained by using SVM for classification. A series of experiments by using different divided methods are designed to evaluate the effects of characteristics which are extracted by K-ELBP algorithm. According to the results of the experiments, the proposed K-ELBP algorithm can extract facial expression features effectively, and the rates of recognition are satisfying.