School of Cyber Engineering, Xidian University, Xi'an 710071, China.
Shaanxi Key Laboratory of Network and System Security, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2018 Aug 31;18(9):2894. doi: 10.3390/s18092894.
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, and , 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, calculates the gaze-coordinates error which is treated as a . To solve the imprecise gaze-coordinates caused by the low resolution of the frontal camera, we further take advantage of to extract the between consecutive frames. In order to resist traditional attacks (shoulder surfing and impersonation attacks) during the procedure of mobile device authentication, we innovatively combine and 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.
我们提出了一种双流模型，利用反射性眼球运动进行智能移动设备认证。我们的模型基于两个预先训练的神经网络和，针对两个独立的任务：（i）注视跟踪和（ii）未来帧预测。我们设计了一个程序，在移动设备的屏幕上随机生成视觉刺激，而前置摄像头将同时捕捉用户的头部运动，以便观看。然后，计算注视坐标误差，将其视为。为了解决由于前置摄像头分辨率低而导致的不精确注视坐标问题，我们进一步利用来提取连续帧之间的。为了抵抗移动设备认证过程中的传统攻击（肩窥和冒充攻击），我们创新性地结合和来训练一个 2 类支持向量机（SVM）分类器。实验结果表明，该分类器在认证移动设备用户身份方面的准确率达到了 98.6%。
