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Conditional Mutual Information Based Boosting for Facial Expression Recognition (2005)
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BibTeX
@MISC{Shan05conditionalmutual,
author = {Caifeng Shan and Shaogang Gong and Peter W. McOwan},
title = {Conditional Mutual Information Based Boosting for Facial Expression Recognition},
year = {2005}
}
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Abstract
This paper proposes a novel approach for facial expression recognition by boosting Local Binary Patterns (LBP) based classifiers. Low-cost LBP features are introduced to effectively describle local features of face images. A novel learning procedure, Conditional Mutual Infomation based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifiers that maximize their mutual information about a candidate class, conditional to the response of any weak classifier already selected; a strong classifier is constructed by combining the learned weak classifiers using the Naive-Bayes. Extensive experiments on the Cohn-Kanade database illustrated that LBP features are effective for expression analysis, and CMIB enables much faster training than AdaBoost, and yields a classifier of improved classification performance.
Keyphrases
facial expression recognition conditional mutual information weak classifier face image mutual information learned weak classifier candidate class low-cost lbp feature cohn-kanade database describle local feature conditional mutual infomation novel approach improved classification performance lbp feature local binary pattern novel learning procedure extensive experiment strong classifier expression analysis
