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...
Saved in:
| Main Authors: | , , |
|---|---|
| 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 |
| 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. |
|---|