| Abstract |
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Facial emotion is vital path for human contact and also used in numerous real applications. Facial expression identification has in recent times become a hopeful investigate area. Their applications include human-computer interface, human emotion examination robot control, driver state surveillance and medical fields. This paper aims to perform emotion classification scheme to identify six dissimilar facial emotions, such as anger, fear, sad, happy, disgust and surprise by using JAFFE database. This was done by extracting patch based type from the image by using PCA. After patches are matched to the trained image. The scheme of patch harmonizing operations has been used to construct features for object identification and action categorization, which stay on vigorous when there are change in location, scale, and direction. The smallest value is chosen as the last feature for emotion categorization. The consequential distance characteristics are fed into the Random Forest Classifier to get accurate results for emotion recognition |
