Abstract
The face is the most informative sign in a people's recognition. The face contains such features as identity, gender, race, mood, attention and emotions. Face recognition is critical for some services, while recent research shows that people recognition can be very different from person to person.In the past few years, a new type of algorithm Generative Adversarial Network (GAN) has appeared that allows you to generate artificial faces that are identical to real faces. This algorithm is currently widely used in the generation of new faces for marketing campaigns, video processing, increasing the resolution of images, as well as in entertainment applications.This study focuses on the effectiveness of recognizing, distinguishing and memorizing real and fake faces. In the introduction, a literature review is presented. It covers issues of decision-making by people, face recognition, and factors affecting the memorization of faces. The second part contains a description of research methodology - data collection, research design, concerns the work (collection, analysis) with data and procedures. Further hypotheses are put forward and the analysis and conclusion are given.