Abstract
We introduce a two-stream model to use reflexive eye movements for smart mobile device authentication. Our model is based on two pre-trained neural networks, iTracker and PredNet, targeting two independent tasks: (i) gaze tracking and (ii) future frame prediction. We design a procedure to randomly generate the visual stimulus on the screen of mobile device, and the frontal camera will simultaneously capture head motions of the user as one watches it. Then, iTracker calculates the gaze-coordinates error which is treated as a static feature. To solve the imprecise gaze-coordinates caused by the low resolution of the frontal camera, we further take advantage of PredNet to extract the dynamic features between consecutive frames. In order to resist traditional attacks (shoulder surfing and impersonation attacks) during the procedure of mobile device authentication, we innovatively combine static features and dynamic features to train a 2-class support vector machine (SVM) classifier. The experiment results show that the classifier achieves accuracy of 98.6% to authenticate the user identity of mobile devices.
Highlights
In the era of the Mobile Internet, a large amount of private information is stored in smart mobile devices [1,2,3,4,5,6], which make the authentication of users a vital precondition of the secure access to the sensitive data
We provide higher security since the traditional biometric authentication methods are cheated by impersonation attacks
We proposed a novel method to use reflexive eye movements for smart mobile device authentication
Summary
In the era of the Mobile Internet, a large amount of private information is stored in smart mobile devices [1,2,3,4,5,6], which make the authentication of users a vital precondition of the secure access to the sensitive data. Fingerprint and face recognition methods can defend shoulder-surfing, impersonation attacks still exist [12]. Eye tracking has been used in some emerging fields such as human–computer interaction [13,14,15,16] and computer vision [17,18] as an important technique across many domains with a series of decent research results. Among those results, an authentication method exploiting gaze-based information is easy to implement relying on the high precision of dedicated devices
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