Show simple item record

dc.contributor.authorMuthukaruppan, Karthigayan
dc.contributor.authorRamachandran, Nagarajan
dc.contributor.authorMohd Rizon, Mohamed Juhari
dc.contributor.authorSazali, Yaacob
dc.date.accessioned2009-08-03T08:59:37Z
dc.date.available2009-08-03T08:59:37Z
dc.date.issued2007
dc.identifier.citationp.57-60en_US
dc.identifier.isbn978-0-7695-2994-1
dc.identifier.urihttp://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4457492
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/6670
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.orgen_US
dc.description.abstractIn this paper, lip and eye features are applied to classify the human emotion through a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA). South East Asian face is considered in this study. All six universally accepted emotions and one neutral are considered for classifications. The method which is fastest in extracting lip features is adopted in this study. Observation of various emotions of the subject lead to an unique characteristic of lips and eye. GA is adopted to optimize irregular ellipse and regular ellipse characteristics of the lip and eye features in each emotion respectively. The GA method approach has achieved reasonably successful classification of emotion. While performing classification, optimized values can mess or overlap with other emotions range. In order to overcome the overlapping problem between the emotions and at the same time to improve the classification, a neural network (NN) approach is implemented. The GA-NN based process exhibits a range of 83% - 90% classification of the emotion from the optimized feature of top lip, bottom lip and eye.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineering (IEEE)en_US
dc.relation.ispartofseriesProceedings of 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2007)en_US
dc.subjectGenetic algorithm (GA)en_US
dc.subjectEmotion recognitionen_US
dc.subjectFace recognitionen_US
dc.subjectImage classificationen_US
dc.subjectHuman face recognition (Computer science)en_US
dc.subjectImage processingen_US
dc.titlePersonalized face emotion classification using optimized data of three featuresen_US
dc.typeArticleen_US


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record