Name
Abstract | ||
|---|---|---|
This paper presents a novel approach to LBP feature extraction. Unlike other LBP feature extraction methods, we evolve the number, position, and the size of the areas of feature extraction. The approach described in this paper also attempts to minimize the number of areas as well as the size in an effort to reduce the total number of features needed for LBP-based face recognition. In addition to reducing the number of features by 63%, our approach also increases recognition accuracy from an average of 99.04% to 99.84%. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2016039.2016092 | ACM Southeast Regional Conference 2005 |
Keywords | Field | DocType |
facial recognition,novel approach,lbp feature extraction method,recognition accuracy,feature extraction,total number,lbp-based face recognition,lbp feature extraction,manhattan distance,local binary pattern,face recognition | Facial recognition system,Computer vision,Pattern recognition,Computer science,Euclidean distance,Local binary patterns,Feature extraction,Artificial intelligence | Conference |
Citations | PageRank | References |
6 | 0.71 | 6 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph Shelton | 1 | 40 | 11.67 |
| Gerry V. Dozier | 2 | 326 | 44.63 |
| Kelvin Bryant | 3 | 52 | 5.56 |
| Joshua Adams | 4 | 78 | 9.83 |
| Khary Popplewell | 5 | 19 | 3.03 |
| Tamirat Abegaz | 6 | 29 | 5.00 |
| Kamilah Purrington | 7 | 8 | 1.10 |
| Damon L. Woodard | 8 | 522 | 31.66 |
| Karl Ricanek | 9 | 165 | 18.65 |