| Summary: | 碩士 === 淡江大學 === 資訊工程學系碩士班 === 104 === Expression recognition in computer vision has a wide range of applications, such as use of infant care, care of the elderly and other human-computer interaction. Accurate facial expression recognition is a crucial key to the successfulness of these applications. In this paper, an algorithm using LBP (Local Binary Pattern) histograms together with Adaboost (Adaptive Boosting) and SVM (Support Vector Machine) is proposed. Given a normalized Image of human face and divide it into overlapping blocks of various sizes. The LBP histogram is used as a texture feature. For each expression, use Adaboost to select the discriminative blocks, and train SVM with these features to achieve recognition single expression. Then, a multiclass SVM is trained to yield a final recognition. We test the proposed algorithm on JAFFE and CK two face image databases and have promising experimental results.
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