3D Face Recognition using Patch Geodesic Derivative Pattern

In this paper, a novel Patch Geodesic Derivative Pattern (PGDP) describing the texture map of a face through its shape data is proposed. Geodesic adjusted textures are encoded into derivative patterns for similarity measurement between two 3D images with different pose and expression variations. An extensive experimental investigation is conducted using the publicly available Bosphorus and BU-3DFE databases covering face recognition under pose and expression changes. The performance of the proposed method is compared with the performance of the state-of-the-art benchmark approaches. The encouraging experimental results demonstrate that the proposed method is a new solution for 3D face recognition in single model databases.