A NEW APPROACH FOR MOOD DETECTION VIA USING PRINCIPAL COMPONENT ANALYSIS AND FISHERFACE ALGORITHM

Rajneesh Singla

Abstract


Natural facial expressions commonly occur in social interactions between people, and are useful for providing an emotional context for the interaction, and for communicating social intentions. This paper depicts an idea regarding detecting an unknown human face from input imagery and recognise his/her current mood. The objective of this paper is that psychological state giving information about some disorders helpful with diagnosis of depression, mania or schizophrenia. The elimination of errors due to reflections in the image has not been implemented but the algorithms used in this paper are computationally efficient to resolve errors. In this paper we have accepted five different moods to be recognized are: Joy, Fear, Contempt, Sad, Disgust and Astonished. Principal Component Analysis (PCA) is implemented with Fisher face Algorithm to recognize different moods.The main part of this paper is an emotional database which will contain images of faces, their corresponding Action Units and their labels. The contribution of this database to the problem stated above is that it can be used by systems in order to recognize emotional facial expressions given one of the database data i.e. action units’ combination
Keywords--- Feature Extraction, Facial Expression Detection, Principal Component analysis (PCA), Fisher face Algorithm

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