Facial expression recognition using ELBP based on covariance matrix transform in KLT
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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.
KeywordsFacial expression recognition Extended local binary pattern Covariance matrix transform KLT Machine learning
This work was supported by the National Natural Science Foundation of China (No.11172342, 11372167), the Fundamental Research Funds for the Central Universities (No.GK201405007), Interdisciplinary Incubation Project of Learning Science of Shaanxi Normal University, the Program of Key Science and Technology Innovation Team in Shaanxi Province (No. 2014KTC-18).
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