Human facial expressions describe a set of signals, which can be associated with mental states such as emotions depending on physiological conditions. There are many potential applications of expression recognition systems. They take into account about two hundred emotional states. Expression recognition is a challenging problem, not only due to the variety of expressions, but also due to difficulty in extraction of effective features from facial images. Depending on a 3D reconstruction technique, 3D data can be immune to a great range of illumination and texture variations, and they are no sensitive as 2D images to out-of-plane rotations. Moreover, 2D images may fail to capture subtle but discriminative change on the face if there is no sufficient change in brightness, such as bulges on the cheeks and protrusion of the lips. In fact, 3D data yield better recognition than conventional 2D data for many types of facial actions The most effective tool for solution of the problem of human face recognition is neural networks. But the result of recognition can be spoiled by facial expressions and other deviation from canonical face representation. In the proposed presentation we describe a resampling method of human faces represented by 3D point clouds. The method based on the non-rigid ICP (Iterative Closest Point) algorithm. We consider the combined using of this method and convolutional neural network (CNN) in the face recognition task. Computer simulation results are provided to illustrate the performance of the proposed algorithm.