Managing the uncertainty for face classification with 3D features

Abstract

This paper describes an original methodology for the improvement of the reliability of results in classification systems based on 3D images. More in detail, it is based on the knowledge of the uncertainty of the features constituting the 3D image and on a suitable statistical approach providing a confidence level to the classification result. These pieces of information are then managed in order to improve the classification performance. The first experiments show that, compared with a traditional approach (which generally does not take into account the uncertainty on 3D features), the proposed methodology allows to significantly improve the classification performance even in a scenario characterized by a high uncertainty.