Expression robust 3D face recognition by matching multi-component local shape descriptors on the nasal and adjoining cheek regions

This paper proposes a novel local depth and surface normals descriptor to explore the discriminative features on the nasal surface and the adjoining cheek regions for expression robust 3D face recognition. After preprocessing the 3D face data, landmarks located on the perimeter of a triangular region covering the nose and adjoining parts of the cheeks are accurately detected. Inspired by Local Binary Patterns, local shape differences for 3D points on a set of horizontal curves joining selected landmarks provide a novel representation of the local shape information. A further analysis of the discriminatory power of each patch shows that the adjoining regions have the potential to produce good recognition performance. Using the FRGC and Bosphorus databases, the performance of the proposed descriptor is evaluated on diverse patches, scales and for four components, one from the depth and three from the surface normals. Results show that the new local shape descriptor performs well at representing the shape information on a relatively large scale. On the basis of this descriptor, a relatively small set of features extracted from the nasal and adjoining cheek regions produce a R₁RR of 97.76% and an EER of 1.32%. The adjoining cheek regions demonstrate a high discriminatory power and provide a useful new addition to 3D face biometrics.