A Facial Expression Classification System Based on Active Shape Model and Support Vector Machine

Most traditional expression classification systems track facial component regions such as eyes, eyebrows, and mouth for feature extraction. This paper utilized facial components to locate dynamic facial textures such as frown lines, nose wrinkle patterns, and nasolabial folds to classify facial expressions. Adaboost using Haar-like feature and Active Shape Model (ASM) are adopted to accurately detect face and acquire important facial feature regions. Gabor filter and Laplacian of Gaussian are used to extract texture information in the acquired feature regions. These texture feature vectors represent the changes of facial texture from one expression to another expression. Support Vector Machine is deployed to classify the six facial expression types including neutral, happiness, surprise, anger, disgust, and fear. Cohn-Kanade database was used to test the feasibility of proposed method and the average recognition rate reached 91.7%.

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