Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/KST.2016.7440531 | 2016 8th International Conference on Knowledge and Smart Technology (KST) |
Keywords | DocType | ISSN |
string grammar,K-nearest neighbor,string grammar fuzzy K-nearest neighbor,face recognition,ORL,MIT-CBCL,Georgia Tech face database,FEI,JAFFE | Conference | 2374-314X |
ISBN | Citations | PageRank |
978-1-4673-8137-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Payungsak Kasemsumran | 1 | 0 | 0.34 |
| S. Auephanwiriyakul | 2 | 246 | 39.45 |
| Nipon Theera-umpon | 3 | 184 | 30.59 |