Conditional Mutual Infomation Based Boosting for Facial Expression Recognition
Caifeng Shan, Shaogang Gong, Peter William McOwan · 2005
This paper proposes a novel approach for facial expression recognition by boosting Local Binary Patterns (LBP) based classifiers. L ow-cost LBP features are introduced to effectively describle local fea tures of face images. A novel learning procedure, Conditional Mutual Infomation based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifie rs that maximize their mutual information about a candidate class, conditional to the response of any weak classifier already selected; a strong cl assifier 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 c lassification performance.