3D face recognition: Multi-scale strategy based on geometric and local descriptors

Most human expression variations cause a non-rigid deformation of face scans, which is a challenge today. In this article, we present a novel framework for 3D face recognition that uses a geometry and local shape descriptor in a matching process to overcome the distortions caused by expressions in faces. This algorithm consists of four major components. First, the 3D face model is presented at different scales. Second, isometric-invariant features on each scale are extracted. Third, the geometric information is obtained on the 3D surface in terms of radial and level facial curves. Fourth, the feature vectors on each scale are concatenated with their corresponding geometric information. We conducted a number of experiments using two well-known and challenging datasets, namely, the GavabDB and Bosphorus datasets, and superior recognition performance has been achieved. The new system displays an overall rank-1 identification rate of 98.9% for all faces with neutral and non-neutral expressions on the GavabDB database.