Ідентифікація окуло-моторної системи людини на основі ряду Вольтерри: застосування в системі захисту інформації

• Видавець: Kamianets-Podilskyi National Ivan Ohiienko University
• Дата: 2022
• Автори: Павленко, Віталій, Шаманіна, Тетяна, Чорі, Владислав
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: Kamianets-Podilskyi National Ivan Ohiienko University 2022
• Онлайн доступ: http://mcm-tech.kpnu.edu.ua/article/view/269343

The information technology of biometric identification of a person has received further development due to the use as a source of primary data of information models of the oculo-motor system (OMS) of the «input-output» type based on the Volterra series. Eye-tracking technology is used to build models. Experimental studies of the OMS of two respondents were carried out. Based on the data obtained with the Tobii Pro TX300 eye-tracker, the transient functions of the first, second and third orders of the OMS when applying the Volterra series model were determined. This makes it possible to increase the accuracy of OMS modeling and, as a result, to increase the reliability of recognition in the space of the proposed heuristic features, which are determined using integral and differential transformations of multidimensional transient functions of OMS, which greatly simplifies the identification of features and the practical implementation of the Bayesian classifier. A high variability of the transient functions of the second and third orders for two respondents was revealed. Thus, it seems appropriate to use multidimensional transient functions for biometric identification. A set of heuristic features are proposed, which are determined on the basis of multidimensional transient functions obtained from eye-tracking data. The informativeness of individual features and their combinations in pairs was investigated. Two-dimensional feature spaces with the maximum value of the probability of correct recognition indicator when solving the problem of biometric identification of a person were found (Pmax = 0.974). The research results were obtained using the construction of Bayesian classifiers in different spaces of the proposed features by means of machine learning based on the data of the formed training samples.