Scientific Journal of KubSAU

One of the key areas of interdisciplinary research is to ensure public safety. In order to solve a number of problems within this area we can effectively use information technology and, in particular, an automated pattern recognition technology and identification of objects on digital images. There are addition problems in objects identifying process besides eliminating the influence of ambient light, angle, items of clothing and headgear. To ensure the applicability of the recognition approach to public security issues it must meet requirements of the high processing speed, the replenishment capabilities onthe-fly list of known images, and the low computational complexity of algorithms. The article deals with the main approaches to the recognition and identification of objects on digital images based on statistical approaches, as well as neural network models. We have allocate their basic features and principles, provided a brief description of each method. Consideration has been made in terms of the application for the problems of public safety, in which there is importance of the speed of the identification of the object, the ability of quickly learning for the system to accept new images and simultaneously process a plurality of input images. The analysis of the existing approaches has shown that none of them satisfy at least one or several needs, which are defined by domain problems of public safety