АNALYSIS OF FACE IDENTIFICATION TECHNOLOGIES IN THE IMAGE

Abstract

The article is devoted to research and analysis of face identification methods in images. Current approaches and technologies used for recognizing persons in large volumes of data are considered. In particular, the methods of machine learning, neural networks and computer vision are investigated, which are used to improve the accuracy of identification and recognition of persons in images. Image face recognition is a computer vision task aimed at recognizing and classifying faces of people or other objects in photos or videos. The task of face recognition has many applications in fields such as biometrics, organization of video conferences, machine vision systems in robotics, intelligent security and access control systems, etc. The task of detecting a face in an image is often the first step in the process of solving a higher-level problem – recognition of a face, details of a face or facial expressions. In addition, information about the presence and number of people in the image can be useful in systems of automatic accounting of the number of visitors; access control systems in institutions, airports and subways; automatic accident prevention systems; intelligent human-computer interfaces; in photography for automatic focusing on a person’s face, as well as for stabilizing the image of a face in order to facilitate the recognition of emotions; to expand the area of stereo vision during the creation of 3D display systems Analysis of face identification methods on images plays an important role in the development of computer vision and pattern recognition technologies. The article highlights the main challenges in this direction, such as ensuring resistance to changes in lighting, facial position and external factors. Issues of privacy and ethical aspects of the use of facial recognition technologies in various fields, including security, medicine and social systems, are also explored. An overview of modern achievements in the field of face identification is provided and the prospects for further research in this direction are indicated. The work may be useful for specialists in the field of computer vision, machine learning, as well as for those interested in the ethical aspects of the use of face recognition technologies.