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dc.contributor.authorPayungsak Kasemsumranen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.description.abstract© Springer International Publishing Switzerland 2016. Facial expression recognition can provide rich emotional information for human computer interaction. It has become more and more interesting problem recently. Therefore, we propose a facial expression recognition system using the string grammar fuzzy K-nearest neighbor. We test our algorithm on 3 data sets, i.e., the Japanese Female Facial Expression (JAFFE), the Yale, and the Project- Face In Action (FIA) Face Video Database, AMP, CMU (CMU AMP) face expression databases. The system yields 89.67 %, 61.80 %, and 96.82 % in JAFFE, Yale and CMU AMP, respectively. We compare our results indirectly with the existing algorithms as well. We consider that our algorithm provides comparable results with those existing algorithms but we do not need to crop an image beforehand.en_US
dc.subjectComputer Scienceen_US
dc.titleFacial expression recognition using string grammar fuzzy K-nearest neighboren_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume9787en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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