Continuous Age Detection using Keystroke Dynamics

Abstract

When enrolling users into computer systems and applications that are restricted to certain age groups, it is challenging to put trust in the user's provided age. This paper looks into the deployment of continuous analysis of Keystroke Dynamics data captured from the online activity of a user. Using this data the goal is to categorize the user's age into two possible categories: above and below the age of 18, a widespread legal age. We used a dataset captured from 70 adults and 46 children, containing over 780.000 keystrokes. The data is collected when the participants were chatting with a random other participant. Two different statistical methods, using timing features, are presented in both an authentication and an identification scenario. In the authentication scenario we reached an average accuracy of approximately 80% after on average of 180 keystrokes, while in the identification scenario we obtained a 75% True Positive Rate after approximately 20 keystrokes.