Learning mixture models for gender classification based on facial surface normals

The aim in this paper is to show how to discriminate gender using a parameterized representation of fields of facial surface normals (needle-maps). We make use of principle geodesic analysis (PGA) to parameterize the facial needle-maps. Using feature selection, we determine the selected feature set which gives the best result in distinguishing gender. Using the EM algorithm we distinguish gender by fitting a two component mixture model to the vectors of selected features. Results on real-world data reveal that the method gives accurate gender discrimination results