Method of Facial Geometric Feature Representation for Information Security Systems

Abstract

Throughout human history, emotional manifestations have played a major role in interpersonal interaction among humans in all areas of society. In particular, information security systems for visual surveillance, based on recognizing emotional states by facial expressions, have recently become highly relevant. In this paper, we propose a method of representing geometric facial features, which aims to enhance the functioning of visual surveillance for information security systems. The method is designed to automatically reflect the facial expressions of human emotions in the form of quantitative characteristics of geometric shapes. It uses software-generated landmarks for constructing specific geometric characteristics of the face, which serve as input data for the method. Our method consists in forming seven geometric shapes based on predefined landmarks, with the subsequent quantitative expression of these shapes. The method derives quantitative features of seven forms, which are further used to identify emotional facial states. We validated the proposed method using hyperplane classification and compared its performance with analogs. As such, the classification model, which was constructed based on the proposed method, achieved a classification accuracy of 92.73% and slightly surpassed the analogs in other statistical indicators. Overall, the results of computational experiments confirmed the effectiveness of the proposed method for identifying changes in a person’s emotional state by facial expressions. In addition, the use of simple mathematical calculations in our method has significantly reduced the computational complexity against analogs.