Semi-supervised Feature Selection for Gender Classification

We apply a semi-supervised learning method to perform gender determination. The aim is to select the most discriminating feature components from the eigen-feature representation of faces. By making use of the information provided by both labeled and unlabeled data, we successfully reduce the size of the labeled data set required for gender feature selection, and improve the classification accuracy. Instead of using 2D brightness images, we use 2.5D facial needle-maps which reveal more directly facial shape information. Principal geodesic analysis (PGA), which is a generalization of principal component analysis (PCA) from data residing in a Euclidean space to data residing on a manifold, is used to obtain the eigen-feature representation of the facial needle-maps. In our experiments, we achieve 90.50% classification accuracy when 50% of the data are labeled. This performance demonstrates the effectiveness of this method for gender classification using a small labeled set, and the feasibility of gender classification using the facial shape information.