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...

全面介紹

Saved in:
書目詳細資料
Main Authors: Payungsak Kasemsumran, Sansanee Auephanwiriyakul, Nipon Theera-Umpon
格式: Conference Proceeding
出版: 2018
主題:
在線閱讀:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966534385&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/55525
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
機構: Chiang Mai University
實物特徵
總結:© 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.