Accurate Extraction of Corresponding Surface Normal Vectors by Point Cloud Partitioning for 3D Face Recognition under Expression Variation

In holistic-based 3D face recognition methods, which have been shown to be more promising than feature-based methods, the most commonly used feature for recognition is the 3D coordinate of the face points. According to the experiments in this work, surface normal vectors alone have more discriminative information than the coordinates, and utilizing them along with the coordinates of the points improves the recognition. However, because of the variation in the aspect ratio of the face of different individuals, registering the points of a face all together to the reference face does not result in an appropriate correspondence between their points. This outcome in particular affects the quality of the extracted surface normal vectors and consequently degrades the recognition performance. In this paper, it has been shown that by partitioning the point cloud of a face into smaller parts and then registering each part separately to the reference face, the recognition performance can be significantly improved.