Face recognition using string grammar fuzzy K-nearest neighbor

Payungsak Kasemsumran; Sansanee Auephanwiriyakul; Nipon Theera-Umpon

Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55525

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DC Field	Value	Language
dc.contributor.author	Payungsak Kasemsumran	en_US
dc.contributor.author	Sansanee Auephanwiriyakul	en_US
dc.contributor.author	Nipon Theera-Umpon	en_US
dc.date.accessioned	2018-09-05T02:57:34Z	-
dc.date.available	2018-09-05T02:57:34Z	-
dc.date.issued	2016-03-23	en_US
dc.identifier.other	2-s2.0-84966534385	en_US
dc.identifier.other	10.1109/KST.2016.7440531	en_US
dc.identifier.uri	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966534385&origin=inward	en_US
dc.identifier.uri	http://cmuir.cmu.ac.th/jspui/handle/6653943832/55525	-
dc.description.abstract	© 2016 IEEE. A string grammar fuzzy K-nearest neighbor is developed by incorporating 2 types of membership value into string grammar K-nearest neighbor. We apply these two string grammar fuzzy K-nearest neighbors in the face recognition system. The system provides 99.25%, 99.75%, 79.57%, 93.85%, and 100% in ORL, MIT-CBCL, Georgia Tech, FEI and JAFFE databases, respectively. Although, the results are satisfied, there are some limitations on the system. It is not scale-invariant. Also, the Levenshtein distance might create misperception between strings that are actually far apart but the calculated distance is small.	en_US
dc.subject	Computer Science	en_US
dc.subject	Medicine	en_US
dc.subject	Social Sciences	en_US
dc.title	Face recognition using string grammar fuzzy K-nearest neighbor	en_US
dc.type	Conference Proceeding	en_US
article.title.sourcetitle	2016 8th International Conference on Knowledge and Smart Technology, KST 2016	en_US
article.stream.affiliations	Chiang Mai University	en_US
Appears in Collections:	CMUL: Journal Articles

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