An efficient three-dimensional face recognition system based random forest and geodesic curves

3D Face recognition is being extensively recognized as a biometric performance refers to its non-intrusive environment. In spite of large research on 2-D face recognition, it suffers from low recognition rate due to illumination variations, pose changes, poor image quality, occlusions and facial expression variations, while 3D face models are insensitive to all these conditions. In this paper, we present an efficient 3D face recognition approach based on Geodesic Distance (GD) of Riemannian geometry and Random Forest (RF), named GD-FM+RF. Therefore, to compute the geodesic distance between the specified pairs of the points of 3D faces, we applied Fast Marching (FM) algorithm, in order to solve the Eikonal equation. Then, these extracted features presented by the geodesic facial curves are used by Principal Component Analysis (PCA) algorithm to analyze class separability. Afterwards, these features were utilized as input of RF classifier. In order to test our approach and assess its effectiveness, simulated series of tests were implemented on 3D SHape REtrieval Contest 2008 database (SHREC'08). As a result, our proposed approach enhances the recognition rate and achieves promising results compared to state of the art methods, getting 99.11% in terms of recognition rate. (C) 2021 Elsevier B.V. All rights reserved.