Gender classification from infants to seniors

Many believe that gender classification is a solved problem, however, gender classification for children is a very difficult problem that has not been adequately addressed by the research community. In this work we demonstrate this fact and present a system that performs gender classification on children that outperforms humans. Motivated by the significant improvement in model selection for age estimation, we investigate a robust gender classification system via model selection and evaluate the systems using leave-one-person-out cross-validation and 5-fold cross-validation schemes on FG-NET database. Furthermore, this work develops a novel operator, graph gender preserving, to build a neighborhood graph for locality preserving projection for gender classification.