期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2015
卷号：8
期号：6
页码：57-64
DOI：10.14257/ijhit.2015.8.6.06
出版社：SERSC
摘要：The accuracy of facial expression feature extraction directly influences the recognition rate of facial expression. In order to extract facial expression feature effectively, this paper puts forward a new way of facial expression feature extraction ensemble learning algorithm based on ensemble thinking. The superposition method of heteromorphy ensemble learning is used to construct an ensemble learning model, two-d gabor wavelet, block local binary patterns and two-directional two-dimensional principal component analysis as the single learning device. Firstly, two-d gabor wavelet was used to get image texture information at each level. Then the image was divided into some blocks to acquire the eigenvectors of block local binary patterns, and reduced its dimensionality. Next, the dimensionality was further reduced by two-directional two-dimensional principal component analysis, extracted the effective characteristic features at the same time. Finally, it is classified by nearest neighbor classifier on the extracted feature library. Experimental results on JAFFE expression database show that this ensemble learning model gets higher recognition rate and better generalization ability than single learning device .
