An Effective Gaze-Based Authentication Method with the Spatiotemporal Feature of Eye Movement

Abstract

Eye movement has become a new behavioral feature for biometric authentication. In the eye movement-based authentication methods that use temporal features and artificial design features, the required duration of eye movement recordings are too long to be applied. Therefore, this study aims at using eye movement recordings with shorter duration to realize authentication. And we give out a reasonable eye movement recording duration that should be less than 12 s, referring to the changing pattern of the deviation degree between the gaze point and the stimulus point on the screen. In this study, the temporal motion features of the gaze points and the spatial distribution features of the saccade are using to represent the personal identity. Two datasets are constructed for the experiments, including 5 s and 12 s of eye movement recordings. On the datasets constructed in this paper, the open-set authentication results show that the Equal Error Rate of our proposed methods can reach 10.62% when recording duration is 12 s and 12.48% when recording duration is 5 s. The closed-set authentication results show that the Equal Error Rate of our proposed methods can reach 5.25% when recording duration is 12 s and 7.82% when recording duration is 5 s. It demonstrates that the proposed method provides a reference for the eye movements data-based identity authentication.