Face Recognition Using 2D and 3D Multimodal Local Features

Machine recognition of faces is very challenging because it is an interclass recognition problem and the variation in faces is very low compared to other biometrics. Global features have been extensively used for face recognition however they are sensitive to variations caused by expressions, illumination, pose, occlusions and makeup. We present a novel 3D local feature for automatic face recognition which is robust to these variations. The 3D features are extracted by uniformly sampling local regions of the face in locally defined coordinate bases which makes them invariant to pose. The high descriptiveness of this feature makes it ideal for the challenging task of interclass recognition. In the 2D domain, we use the SIFT descriptor and fuse the results with the 3D approach at the score level. Experiments were performed using the FRGC v2.0 data and the achieved verification rates at 0.001 FAR were 98.5% and 86.0% for faces with neutral and non-neutral expressions respectively.