Application of a cellular neural network to facial expression animation and high‐level image processing

Abstract

The cellular neural network (CNN) is used to animate facial expressions of a human being. First, the change in facial expressions is regarded as a smooth 2D transformation which is restricted by a bending energy function and displacements of some key-points. Second, the parameters of the CNN are determined by comparing the bending energy function with the energy function of the CNN. Finally, the CNN is used to realize the transformation by minimizing its energy function. Also, the CNN is used to model some visual illusions which are frequently used in psychological tests. First, the retinal induction field is modelled by using a template. The comparison of this CNN model with the real physiological measurements is presented. Then, based on this template, the CNN universal machine is used to model four types of visual illusions: subjective contour illusion (Kanizsa illusion), size illusion (Mueller-Lyer illusion, Ponzo illusion), direction and location illusion (angular illusion of location, Poggendorff illusion) and contrast illusion (Herring illusion). Computer simulations are provided for animating facial expressions and modelling visual illusions.