Berretti, StefanoAmor, Boulbaba BenDaoudi, MohamedBimbo, Alberto DelMohamed Daoudi and Tobias Schreck2013-10-212013-10-212010978-3-905674-22-41997-0471https://doi.org/10.2312/3DOR/3DOR10/047-054Facial expression recognition has been addressed mainly working on 2D images or videos. In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original approach is proposed that relies on selecting the minimal-redundancy maximal-relevance features derived from a pool of SIFT feature descriptors computed in correspondence with facial landmarks of depth images. Training a Support Vector Machine for every basic facial expression to be recognized, and combining them to form a multiclass classifier, an average recognition rate of 77.5% on the BU-3DFE database has been obtained. Comparison with competitors approaches using a common experimental setting on the BU-3DFE database, shows that our solution is able to obtain state of the art results.