TouchID

Due to the widespread use of mobile devices, it is essential to authenticate users on mobile devices to prevent sensitive information leakage. In this paper, we propose TouchID, which combinedly uses the touch sensor and the inertial sensor for gesture analysis, to provide a touch gesture based user authentication scheme. Specifically, TouchID utilizes the touch sensor to analyze the on-screen gesture while using the inertial sensor to analyze the device's motion caused by the touch gesture, and then combines the unique features from the on-screen gesture and the device's motion for user authentication. To mitigate the intra-class difference and reduce the inter-class similarity, we propose a spatial alignment method for sensor data and segment the touch gesture into multiple sub-gestures in space domain, to keep the stability of the same user and enhance the discriminability of different users. To provide a uniform representation of touch gestures with different topological structures, we present a four-part based feature selection method, which classifies a touch gesture into a start node, an end node, the turning node(s), and the smooth paths, and then select effective features from these parts based on Fisher Score. In addition, considering the uncertainty of user's postures, which may change the sensor data of same touch gesture, we propose a multi-threshold kNN based model to adaptively tolerate the posture difference for user authentication. Finally, we implement TouchID on commercial smartphones and conduct extensive experiments to evaluate TouchID. The experiment results show that TouchID can achieve a good performance for user authentication, i.e., having a low equal error rate of 4.90%.