PCA for gender estimation: which eigenvectors contribute?

A pruning schema is applied to multi-layer perceptron (MLP) gender classifier MLP uses eigenvector coefficients of the face space created by principal component analysis (PCA). We show that pruning improves the initial MLP performance by preserving the most effective input while eliminating most of the units and connections. Pruning is also used as a tool to monitor which eigenvectors contribute to gender estimation. In addition, by usage of FERET face database, we test the PCA approach on gender estimation task in a bigger setting than the previous experiments.