Name
Abstract | ||
|---|---|---|
In this paper, we investigate the use of Genetic and Evolutionary Computations (GECs) for feature selection (GEFeS), weighting (GEFeW), and hybrid weighting/selection (GEFeWS) in an attempt to increase recognition accuracy as well as reduce the number of features needed for biometric recognition. These GEC-based methods were first applied to a subset of 105 subjects taken from the Facial Recognition Grand Challenge (FRGC) dataset (FRGC-105) where several feature masks were evolved. The resulting feature masks were then tested on a larger subset taken from the FRGC dataset (FRGC-209) in an effort to investigate how well they generalise to unseen subjects. The results suggest that our GEC-based methods are effective in increasing the recognition accuracy and reducing the features needed for recognition. In addition, the evolved FRGC-105 feature masks generalised well on the unseen subjects within the FRGC-209 dataset. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1504/IJBM.2012.047642 | IJBM |
Keywords | Field | DocType |
genetic and evolutionary computation, biometrics, feature selection, feature weighting | Facial recognition system,Weighting,Pattern recognition,Feature selection,Feature (computer vision),Computer science,Feature (machine learning),Artificial intelligence,Biometrics,Machine learning,Computation | Journal |
Volume | Issue | ISSN |
4 | 3 | 1755-8301 |
Citations | PageRank | References |
1 | 0.38 | 27 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aniesha Alford | 1 | 32 | 5.91 |
| Kelvin Bryant | 2 | 52 | 5.56 |
| Tamirat Abegaz | 3 | 29 | 5.00 |
| Gerry V. Dozier | 4 | 326 | 44.63 |
| John C. Kelly | 5 | 76 | 15.77 |
| Joseph Shelton | 6 | 40 | 11.67 |
| Lasanio Small | 7 | 1 | 0.72 |
| Jared Williams | 8 | 6 | 1.41 |
| Damon L. Woodard | 9 | 522 | 31.66 |
| Karl Ricanek | 10 | 165 | 18.65 |