User Authentication Using Password and Hand Gesture with Leap Motion Sensor

Abstract

User authentication is becoming a significant factor in the field of modern technology. It is a process that permits a device to confirm the recognition of somebody who interfaces with a system asset. In the world of AI, machine learning is currently one of the leading research fields which is looking into practical implementation. In this paper, we propose a method where the user will enter the given password while the leap motion sensor will compare the behavioral data of the user with an existing dataset. Leap motion controller is a sensor which can recognize 3D movement of hands, fingers, and finger-like object with no contact. For our project, we chose to use dynamic time warping and Naive Bayes classifier algorithm. For the experiment, we captured the leap motion data of eight people and stored them in the database for user authentication and another eight people were used as third-party users. Moreover, as the password is being typed into the keyboard, it is important to restrict the intrusion of malicious third-party users. The proposed system demonstrates about 91% accuracy which rises to 93% in the best-case scenario.