Аннотации:
Currently, the face recognition system is applied in various fields such as classified
materials, file transferring, and general human interaction and innovative technologies,
including cell phones with owner recognition function. The relevance of this research lies
in the need to consider problems touching upon the increase in the efficiency of such
technologies. The objective of this study was to improve the algorithm of the face
identification system from different sides in order to demonstrate appropriate results. In the
course of the study, a method based upon deep neural networks was applied through the
projection of layers. As a result, a receiver operating characteristic curve was constructed
to evaluate the quality of binary classification, and loss functions for deep learning, data
distribution, and algorithm accuracy were demonstrated. Proceeding from the results
obtained, it was found that the selected face identification system is resistant to each of the
influencing factors (make-up, lighting, posture, etc.) considered separately in the work. The
operation principle represents an original technical solution for face recognition problems
when images in the database have discrepancies, which is a typical scenario in the actual
world. The testing accuracy in this work reaches 95.04%.
