Minimal-Redundancy Maximal-Relevance Selection of Facial Regions for 3D Face Recognition

Among different approaches for 3D face recognition, solutions based on local facial characteristics are very promising mainly because they can manage facial expression variations by processing differently distinct parts of the face. However, so far a few works have investigated the individual relevance that local features play in 3D face recognition, with very simple solutions applied in the practice. In this paper, we propose to combine the Minimal-Redundancy Maximal-Relevance feature selection technique, with a 3D face recognition solution based on isogeodesic regions identified around the nose tip. Combination of the two models supports the estimation of the relative relevance of different regions of the face for the purpose of discriminating between different subjects. The proposed solution is experimented using facial scans of the Face Recognition Grand Challenge dataset. Results of the experimentation are twofold in that they quantitatively demonstrate the assumption that different regions of the face have different relevance for face discrimination, and also show that the relevance of these facial regions changes for different ethnic groups.