The analysis of approaches to identify people with digital images in the task of ensuring public safety

Alexey Samoylov, Sergey Kucherov


One of the key areas of interdisciplinary research is to ensure public safety. In order to solve a number of problems within this area, information technology can be effectively used and, in particular, an automated pattern recognition technology and identification of objects on digital images. There are additional problems in object identification processes besides eliminating the influence of ambient light, angle, items of clothing and headgear. To ensure the applicability of recognition approach to public security issues it must meet requirements of the high processing speed, the replenishment capabilities on-the-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. Finding their basic features and principles and providing a brief description of each method. A consideration is made in terms of the application for the problems of public safety, in which it is important the speed of the identification of the object, the ability to quickly learn new images and simultaneously processing a group of input pictures. The analysis of existing approaches showed that none of them satisfy at least one problem defined by the domain of public safety.

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