Deep Learning Based Face Liveliness Detection

Modern systems of face recognition (FRS) are used in a wide range of computer applications: for user authentication; in email marketing; in social networks; for personal identification and more. However, such technologies are often vulnerable to spoofing attacks. Facial image can be faked in various ways: print a photo; record a video; create a high-quality silicone mask, etc. By presenting a fake to the FRS the attacker has an intention of passing himself off as another person, for instance, trying to get an access to a secure computer system. Face Liveliness Detection solves this problem by detecting whether the person in front of the camera is real or fake. In this article, we explore the possibilities of using deep learning technology for face liveliness detection. We consider several models and setting numerous experiments. As datasets for experiments we use various fake images and for each such dataset we obtained evaluation of effectiveness. In our research, our main goal is to improve basic deep learning architectures using latest technologies to get more accurate model.