Abstract:
"
Computer vision is the field that attempts to gain an understanding of visual context to infer
something about the world using an observed image or visual data. It is one of the most important
components of artificial intelligence and helps to effectively solve many complex real-world
problems. “MaskNet” systems are built with the aim of masked face recognition and abstracting
the complexities involved in problem-solving of the underperformed system due to face mask
occlusions. This dissertation is a result of the project to build a robust face recognition system that
recognizes faces while wearing a face mask. The developed system, MaskNet, works by making
use of the knowledge of deep learning and computer vision. The promising combinations and
settings of algorithms, preprocessing, evaluations and different techniques are taken to build the
most effective algorithm to robust recognition of face while wearing a face mask. It is available as
a complete system and interactions with the user from the graphical interface that provide to system
capabilities of a face recognition system with the capability of identifying masked faces.
"
