APPLICATION OF BOW-DICTIONARIES ON THE BASIS OF KAZE-DESCRIPTORS FOR FORECASTING PERSONAL FACTORS OF SOCIAL NETWORKSUSERS
The article proposes to apply modern methods of automatic image processing as a tool for assessing «Big Five» personal factors: offered extract BoW-dictionaries based on invariant to rotation, displacement, and changes in the lighting of KAZE-descriptors from the images in the profiles of users of the «VKontakte» social network with their processing on artificial neural networks in order to build a tool for predicting the personal factors of users. To form training and test samples we used a database of images and profiles of a social networkusers, made with the direct participation of the Psychological Institute of Russian Academy of Education, questioning was carried out according to the «NEO-FFI» questionnaire. Neural networks are implemented using the Microsoft Cognitive Toolkit (CNTK) library. Various neural network architectures, various activation functions, and learning optimizers have been investigated. A comparison is made with methods of direct neural network image processing. Identified personal factors that correlate with the posted images. The proposed method turned out to be better in the task of predicting personality factors than the approach with two neural networks (direct distribution and convolutional), where the first compresses the data, and the second is already trained on them.