Розпізнавання облич за допомогою згорнутої нейронної мережі

Abstract

Modern trends in security and development of information technologies push forward all spheres of human life. The task of isolating a human face in a natural or artificial setting and subsequent identification has always been among the highest priority tasks for researchers working in the field of machine vision systems and artificial intelligence. In addition, the task of recognition is very relevant in the field of security – both for storing data and for finding criminals on surveillance cameras, and so on. In addition, all recognition systems use neural networks to improve performance, increase efficiency and facilitate the process itself. However, at present, despite the similarity of tasks and methods used in the development of alternative systems for biometric identification of a person, such as identification by fingerprints or by the image of the iris, the identification systems by the image of the face are significantly inferior to the above systems. Therefore, improving face recognition systems has many improvement paths. In the work, an analysis of literary publications, existing algorithms used in face recognition and human identification was carried out. The main method of face recognition is the use of a convolutional neural network, the selection of objects in the image is carried out using the Viola-Jones method, the AdaBoost machine learning algorithm is used, and the Haar classifier is most often used as a classifier. The article is devoted to the creation of software for face recognition using a convolutional neural network in real time. The software can recognize and identify a person with head tilt, tilt, and under different lighting conditions. In this case, sampling training for the model is carried out on a limited number of photographs. Experimental studies were carried out to test the developed mathematical model and the real-time face recognition algorithm.