Conditional Mutual Information Based Boosting for Facial Expression Recognition

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 Information 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.