A Comparative Study among Different Computer Vision Algorithms for Assisting Users in Picture Password Composition

Picture gesture authentication (PGA), utilized by millions of users worldwide, is a cued-recall graphical authentication system which requires users to select an image and subsequently draw gestures on that image to create their picture password. A crucial component for enhancing the security of PGA-like schemes is the accurate quantification of the user-chosen passwords through a picture password strength meter. Despite the huge adoption of PGA worldwide, there is rather limited knowledge on the implementation aspects of an accurate picture password strength meter that would assist users in creating secure picture passwords. In this paper, we present the implementation and evaluation of an assistive picture password strength meter system within PGA-like schemes, which is based on image analysis through computer vision techniques. Results of the evaluation study (n=34) revealed that different computer vision approaches perform different across various datasets used during training. These findings could drive the design of intelligent security mechanisms for quantifying the strength of the user-chosen passwords, and ultimately assist end-users towards making better picture password selections by providing feedback about the strength of their passwords, as well as assist service providers in terms of integration of assistive security mechanisms.