Face recognition using string grammar fuzzy K-nearest neighbor

© 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...

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Main Authors: Kasemsumran P., Auephanwiriyakul S., Theera-Umpon N.
Format: Conference Proceeding
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966534385&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/42018
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Institution: Chiang Mai University
id th-cmuir.6653943832-42018
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spelling th-cmuir.6653943832-420182017-09-28T04:24:46Z Face recognition using string grammar fuzzy K-nearest neighbor Kasemsumran P. Auephanwiriyakul S. Theera-Umpon N. © 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. 2017-09-28T04:24:46Z 2017-09-28T04:24:46Z 2016-03-23 Conference Proceeding 2-s2.0-84966534385 10.1109/KST.2016.7440531 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966534385&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42018
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description © 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.
format Conference Proceeding
author Kasemsumran P.
Auephanwiriyakul S.
Theera-Umpon N.
spellingShingle Kasemsumran P.
Auephanwiriyakul S.
Theera-Umpon N.
Face recognition using string grammar fuzzy K-nearest neighbor
author_facet Kasemsumran P.
Auephanwiriyakul S.
Theera-Umpon N.
author_sort Kasemsumran P.
title Face recognition using string grammar fuzzy K-nearest neighbor
title_short Face recognition using string grammar fuzzy K-nearest neighbor
title_full Face recognition using string grammar fuzzy K-nearest neighbor
title_fullStr Face recognition using string grammar fuzzy K-nearest neighbor
title_full_unstemmed Face recognition using string grammar fuzzy K-nearest neighbor
title_sort face recognition using string grammar fuzzy k-nearest neighbor
publishDate 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966534385&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/42018
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