CREATION OF AGE ESTIMATION SYSTEM - KRYVENCHUK, Yurii; YAKYMIV, Roman (Herald of Khmelnytskyi National University. Technical sciences, 2022)
We are surrounded by different transmitting and processing information devices on a daily basis. Analysis of certain selected characteristics of each person allows humanity to achieve previously unthinkable technological development. In particular, the human face. We can predict a person's age, determine his mood or identify someone in the crowd using modern algorithms. As a "window to the soul", the human face provides important information related to its individual traits. With the help of this information, a person can determine such characteristics as ethnic origin, gender, age, and emotions, on the basis of which he is able to take appropriate action, which plays a significant role in non-verbal communication between people. Age estimation is useful in applications where you do not need to specifically identify a person, but we want to know (approximate) their age. With the help of the age approximation system, it is possible to ensure partial digitalization of security control and monitoring systems. With the help of a monitoring camera, the age estiomation system can identify minors and prevent them from entering places where they are not allowed; not to allow minors to buy tobacco products or alcohol from vending machines; to warn the elderly person of possible danger. Age assessment software can also be used in healthcare systems. The authors personally are interested in approximating human age based on images to personalize human-machine interaction. The paper has the description and the results of the creation of an age estimation system, the basis of which is a convolutional neural network. The user can connect with the system through a simple interface which allows you to upload the image for analysis or turn on the camera to get age estimates in real-time. The practical value of this work is the developed high-quality system which is ready for use and implementation in the relevant fields.
Keywords: age approximation; convolutional neural networks; face