Learning Normal Maps for Robust 3D Face Recognition from Kinect Data

Face recognition using 3D scans can be achieved by many approaches, but most of these approaches are based on high quality depth sensors. In this paper, the authors use the normal maps obtained from the Kinect sensor to investigate the usefulness of data augmentation and signal-level fusion derived from depth data captured by a low quality sensor. In this face recognition process, the authors first preprocess the captured 3D scan of each person by cropping the face and reducing the noise; normals are computed and separated into three maps: Nx, Ny, and Nz. the authors combine the three normal maps to form an RGB image; these images are used to train a convolutional neural network. The authors investigate the order of components that yields to the best accuracy and compare it with previous results obtained on CurtinFaces and KinectFaceDB databases, achieving rank one identification rate of 94.04% and 91.35%, respectively.