Mood recognition using combined algorithms and methods (MR CAM)

This paper explores the study of mood recognition in order to aid in activities that concern human-computer interaction. This study is relevant to empathic computing as it should be capable of continuously recognize the emotion and automatically recognize the mood of its users. A lot of existing emotion recognition techniques has been developed to solve the problem of human-computer interaction, however, these emotions only show the feeling of a user in a given instant and not that of the whole time the user has been using the system. This research aims to explore and contribute to this field of study by recognizing the emotion and mood through the use of facial expressions. This research focuses on studying existing techniques on emotion recognition from facial expressions with the use of active shape models. The people exhibit specific emotions in frontal position so as to maximize the observation of facial expressions. Features generated are formed in the 2-D model are then utilized for emotion classification techniques such as naive bayees and sequential minimal optimization. The results of the emotion classifiers play a major role in finding the mood of the person in the video as these results are the factors being considered for the mood recognition algorithm applied in the research