3D Face Recognition Using Joint Differential Invariants

Stemming from a sound mathematical framework dating back to the beginning of the 20th century, this paper introduces a novel approach for 3D face recognition. The proposed technique is based on joint differential invariants, projecting a 3D shape in a 9-dimensional space where the effect of rotation and translation is removed. As a consequence, the matching between two different 3D samples can be directly performed in the invariant space. Thus the matching score can be effectively used to detect surfaces or parts of surfaces characterised by similar when not identical 3D structure. The paper details an efficient procedure for the generation of the invariant signature in the 9-dimensional space, carefully discussing a number of significant implications related to the application of the mathematical framework to the discrete, non-rigid case of interest. Experimental evaluation of the proposed approach is performed over the widely known 3D RMA database, comparing results to the well established Iterative Closest Point (ICP)-based matching approach.