An Evaluation of Gaze-Based Person Identification with Different Stimuli

In eye tracking, the most promising approaches for person identification are based on the iris. Therefore, the iris of a subject is extracted and compared against a database of stored iris templates or directly classified by a deep neural network. While this approach is robust and has found its way into many practical applications like security access devices, it has some limitations, since it is possible to fake the iris. This can be done either by presenting a rendered image or by using a contact lens. Our research focuses on the gaze behavior of a subject for person identification. Therefore, we present different stimuli as well as moving dots. In past research, only a static dot has been used so far. We show that, especially, the combination of different stimuli is a promising approach and could be used as an additional security layer in combination with iris recognition.