Name
Abstract | ||
|---|---|---|
In this paper, we demonstrate the need for dimensionality reduction to mitigate model overfitting on the nontrivial problem of gender classification from digital images. In this study we explore four feature selection schemes using Genetic Algorithm, Memetic Algorithms, and Random Forest, which are fed to a nonlinear support vector machine (SVM) for final classification. The performance of the model (feature) selection approaches are evaluated against two distinct datasets of facial images: FG-NET which contains toddlers to seniors and the UIUC-PAL which contains faces of adults up to seniors. This work demonstrates that feature selection can, and does, improve performance of an SVM based gender classification system significantly. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CIBIM.2011.5949221 | CIBIM |
Keywords | Field | DocType |
random forest,automatic gender classification,dimensionality reduction,model overfitting mitigation,gender issues,nonlinear support vector machine,nontrivial problem,feature extraction,image classification,genetic algorithm,genetic algorithms,svm based gender classification system,memetic algorithm,digital image,feature selection,support vector machines,support vector machine,computational modeling,classification system,indexes,classification algorithms | Dimensionality reduction,Feature selection,Pattern recognition,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Overfitting,Contextual image classification,Statistical classification,Random forest,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-9899-4 | 2 | 0.40 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaw Chang | 1 | 34 | 3.31 |
| Yishi Wang | 2 | 43 | 5.50 |
| Karl Ricanek | 3 | 165 | 18.65 |
| Cuixian Chen | 4 | 53 | 6.38 |