The Method of Using Production Rules in Neural Network Recognition of Emotions by Facial Geometry

The article is devoted to the improvement of neural network means of recognition of emotions on human geometry, which are defined for use in information systems of general purpose. It is shown that modern means of emotional recognition are based on the usual networks of critical disadvantage, because there is a lack of accuracy of recognition under the influence of purchased, characteristic of general-purpose information systems. It is determined that the above remarks relate to the turning of the face and the size of the image. A typical approach to overcoming this disadvantage through training is unacceptable for all protection options that are inappropriate for reasons of duration and compilation of the required training sample. It is proposed to increase the accuracy of recognition by submitting an expert data model to the neural network. An appropriate method for representing expert knowledge is developed. A feature of the method is the use of productive rules and the PNN neural network. Experimental verification of the developed solutions has been carried out. The obtained results allow to increase the efficiency of the termination and disclosure of the set of age networks, the characteristics of which are not presented in the registered statistical data.