Genetic feature subset selection for gender classification: A comparison study

Abstract

We consider the problem of gender classification from frontal facial images using genetic feature subset selection. We argue that feature selection is an important issue in gender classification and demonstrate that Genetic Algorithms (GA) can select good subsets of features (i.e., features that encode mostly gender information), reducing the classification error. First, Principal Component Analysis (PCA) is used to represent each image as a feature vector (i.e., eigen-features) in a low-dimensional space. Genetic Algorithms (GAs) are then employed to select a subset of features from the low-dimensional representation by disregarding certain eigenvectors that do not seem to encode important gender information. Four different classifiers were compared in this study using genetic feature subset selection: a Bayes classifier, a Neural Network (NN) classifier, a Support Vector Machine (SVM) classifier, and a classifier based on Linear Discriminant Analysis (LDA). Our experimental results show a significant error rate reduction in all cases. The best performance was obtained using the SVM classifier. Using only 8.4% of the features in the complete set, the SVM classifier achieved an error rate of 4.7% from an average error rate of 8.9% using manually selected features.