THEORETICAL ASPECTS OF THE DEVELOPMENT OF THE HUMAN FACE RECOGNITION SYSTEM
DOI:
https://doi.org/10.32689/maup.it.2022.1.1Keywords:
recognition, neural networks, recognition methodsAbstract
Abstract. Face Detection System is a technology that compares a human face with a digital image or video frame to a database of individuals, commonly used to authenticate users through identity verification services, and works by accurately identifying and measuring facial features in a given image. Facial recognition systems are used today by governments and private companies around the world, their effectiveness varies, and some systems have previously been written off because of their ineffectiveness. Thus, the creation of a program for human face recognition is a topical issue. The aim of the article is to study the theoretical aspects of developing a human face recognition system. The face recognition procedure simply requires that any device equipped with digital photographic technology generate and obtain the images and data necessary to create and record a biometric image of the person to be identified. The main algorithms of human face recognition are considered: face recognition using different face surfaces, Fisher 's face method, principal components analysis method and machine of reference vectors, Haar cascade method. Their advantages and disadvantages are given. The application of convolutional neural network to face recognition is presented. The implementation of the algorithm of the face recognition system is proposed. This paper analyzes the existing algorithms and systems for face detection and recognition, weighing their advantages and disadvantages. The use of convolutional neural system for facial recognition is considered. The percentage of human face recognition accuracy and performance were analyzed in practice, taking into account factors such as lighting, image quality, number of faces in the image using the face recognition library Face recognition from the DLib family of libraries based on convolutional neural network.
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