The urgency of the task of developing tools for neural network analysis of biometric parameters for recognizing the personality and emotions of students of the distance learning system has been substantiated. The necessity of formalizing the architectural solutions used in the creation of software for neural network analysis of biometric parameters is shown. As a result of the research carried out in terms of the UML modeling language, the architecture of the neural network analyzer of biometric parameters has been developed. Diagrams of options for using the neural network analyzer have been developed both for recognizing the personality of a student when entering the system, and for recognizing the personality and emotions of a student in the process of his interaction with the distance learning system. Also, based on the developed use case diagrams, a structural diagram of the analyzer is built. The necessity of including subsystems for determining the functional parameters of the analyzer, registration of biometric parameters, neural network analysis of registered biometric parameters, personality recognition and emotion recognition is substantiated. An original feature of the proposed architectural solutions is the introduction into the neural network analysis subsystem of an integrated analysis module designed to summarize the results of neural network analysis separately for each of the biometric parameters. A rule for making an integrated decision has been developed, taking into account the results of a neural network analysis of each of the registered biometric parameters and the corresponding weight coefficients determined by expert evaluation. The introduction of the integrated analysis module makes it possible to increase the accuracy of recognition of emotions and personality of a student, since the final classification is realized through a generalized assessment of several guaranteed significant biometric parameters. In addition, the use of this module makes it possible to increase the reliability of the neural network analyzer in case of difficulties associated with the registration of a particular biometric parameter. It has been established that the decision-making rule can be improved by using one or more neural networks in the integrated analysis module, designed to generalize the results of the neural network analysis of all registered biometric parameters. It is proposed to correlate the directions of further research with the development of appropriate neural network solutions.
TEXT-INDEPENDENT PERSONALITY VERIFICATION ON DYNAMIC BIOMETRIC PARAMETERS BASED ON THE KOHONEN NEURAL NETWORK
