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This paper aims to present a multimodal approach in emotion recognition which integrates information from both facial expressions and speech signal. Using two acted databases on different subjects, we were able to emphasize six emotions: sadness, anger, happiness, disgust, fear and neutral state. The models in the system were designed and tested by using a Support Vector Machine classifier. Firstly,...
Support vector machines (SVMs) are powerful classifiers, with very good recognition rates in image analysis tasks. However their computational time in the object recognition phase is often large due to the number of classifications per scene and to the feature vector size, especially when the feature space is formed from raw image data. Several methods are reported in the literature to make the classification...
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