User-dependent models are adapted from the UBM and the recognition score is normalized using the UBM score. We propose a person authentication system using eye movement signals. In security scenarios, eye-tracking has earlier been used for gaze-based password entry. A few authors have also used physical features of eye movement signals for authentication in a taskdependent scenario with matched training and test samples. We propose and implement a task-independent scenario whereby the training and test samples can be arbitrary. We use short-term eye gaze direction to construct feature vectors which are modeled using Gaussian mixtures. The results suggest that there are personspecific features in the eye movements that can be modeled in a task-independent manner. The range of possible applications extends beyond the security-type of authentication to proactive and user-convenience systems.
Kinnunen et al. REF proposed a new person-authentication system using eye movement signals in 2010.
Towards task-independent person authentication using eye movement signals
ETRA '10
Computer Science