| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 99 === In this thesis, a facial expression recognition system which can recognize facial expressions as well as the expression intensity and mixture ratio of basic expressions is developed. Active Appearance Model (AAM) is used to train shape model and texture model. The improved Lucas-Kanade image alignment algorithm is then applied to align the input images to obtain texture features. A novel method is proposed to recognize ratio of basic expressions and intensity of facial expression. Three kinds of texture features are used in this method: 1. texture features of whole face, which are used as inputs of facial expression intensity recognition, 2. texture features of upside face, which are used as inputs of upper face action units recognition, 3. texture features of downside face, which are used as the inputs of lower face action units recognition. Back propagation neural networks are used to obtain the recognition scores, which are then exploited to classify the facial expression results, including basic facial expression ratio and the facial expression intensity.
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