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dc.contributor.authorPiparsaniyan, Yamini-
dc.contributor.authorSharma, V K-
dc.contributor.authorMahapatra, K K-
dc.identifier.citationIEEE International Conference on Communicatoin & SIgnal Processing ICCSP 2014, 3rd-5th April 2014, Adhiparashakti Engineering College, Melmaruvathur, Tamilnadu.en
dc.descriptionCopyright belongs to the Proceeding of Publisheren
dc.description.abstractAutomatic facial expression recognition is important for effective Human computer interaction (HCI) as well as autistic children for communication. In this paper, we propose emotion recognition using Gabor feature and simple Bayesian discriminating classifier based on principal component analysis (PCA) for emotion recognition. The multi class classification strategic has been applied based on highest value of log likelihood after training different emotions class. Facial expression images from JAFFE database have been used for training as well as testing. Very high accuracy (96.73 %) of emotion recognition has been obtained with proposed methoden
dc.format.extent300357 bytes-
dc.subjectBayesian classificationen
dc.subjectComputer visionen
dc.subjectEmotion recognitionen
dc.subjectGabor featuresen
dc.titleRobust Facial Expression Recognition using Gabor Feature and Bayesian Discriminating Classifieren
Appears in Collections:Conference Papers

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