Robust Facial Expression Recognition using Gabor Feature and Bayesian Discriminating Classifier

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2116

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DC Field	Value	Language
dc.contributor.author	Piparsaniyan, Yamini	-
dc.contributor.author	Sharma, V K	-
dc.contributor.author	Mahapatra, K K	-
dc.date.accessioned	2014-04-16T09:14:26Z	-
dc.date.available	2014-04-16T09:14:26Z	-
dc.date.issued	2014-04	-
dc.identifier.citation	IEEE International Conference on Communicatoin & SIgnal Processing ICCSP 2014, 3rd-5th April 2014, Adhiparashakti Engineering College, Melmaruvathur, Tamilnadu.	en
dc.identifier.uri	http://hdl.handle.net/2080/2116	-
dc.description	Copyright belongs to the Proceeding of Publisher	en
dc.description.abstract	Automatic 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 method	en
dc.format.extent	300357 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.subject	Bayesian classification	en
dc.subject	Computer vision	en
dc.subject	Emotion recognition	en
dc.subject	Gabor features	en
dc.subject	PCA	en
dc.title	Robust Facial Expression Recognition using Gabor Feature and Bayesian Discriminating Classifier	en
dc.type	Article	en
Appears in Collections:	Conference Papers

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