A NEURAL NETWORK MODEL FOR HUMAN FACE BOUNDARY DETECTION

Abstract

The relevance of the implementation of means of recognition of the emotional state by the image of the face into the personnel management system is well-founded. It is shown that the implementation of such tools leads to the need to adapt the values of architectural parameters of neural network models for detecting the boundaries of target objects on bitmap images to the expected conditions of use. An approach to determining the most effective type of neural network model is proposed, which involves expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. As a result of the conducted research, it was determined that among the types of neural network models tested in the task of segmentation of raster images, the U-Net model is the most effective for detecting facial borders on small raster images. Using this neural network model provides a mask selection accuracy of 0.88. At the same time, the necessity of improving the mathematical support, which is used to determine the accuracy of face border detection, is determined. It is also advisable to correlate the ways of further research with the correction of typical shortcomings associated with the incorrect marking of the boundaries of various objects that are perceived by the neural network model as a human face.