TouchID:User Authentication on Mobile Devices via Inertial-Touch Gesture Analysis.162:3 二4 Touch sensor Inertial sensor Successtul TTING GESTURE STUR authentication failed CLASSIFIER TRANIN TESTING TESTING Fig.1.A typical application scenario of TouchID The first challenge is how to mitigate the intra-class difference and reduce the inter-class similarity among gestures? To address this challenge,we first conduct extensive experimental studies to observe how the finger moves in a touch gesture and how the device moves caused by the gesture.We find that time differences among touch gestures can affect intra-class difference and inter-class similarity.Therefore,we propose a spatial alignment method to align the sensor data in space domain,and then segment the touch gesture into multiple sub-gestures to highlight the sub-gesture which contributes more for enhancing the stability of the same user and the discriminability of different users. The second challenge is how to represent the touch gesture with different topological structures in a uniform way? The touch gestures corresponding to different graphic patterns have a different number of sub-gestures and the sub-gestures can also be different,which may lead to the different representation of a touch gesture.To address this challenge,we propose a four-part feature selection method,which classifies a touch gesture into four parts, i.e.,a start node,an end node,turning node(s),and smooth paths,whatever the topological structure of the touch gesture is.Then,we select effective features for each part based on Fisher Score.Finally,for each gesture,we can represent it with a feature vector consisted of the uniform feature set. The third challenge is how to tolerate the uncertainty caused by different body postures and hand postures?When performing a touch gesture,the user can sit,lay or stand,and she/he can interact with the device with one hand or two hands,the different postures will lead to the inconsistency of the sensor data for the same touch gesture. To address this challenge,we design a multi-threshold KNN based model to separate the touch gestures under different postures into different clusters adaptively,and then perform user authentication in each cluster.In addition,to reduce the computation overhead of multi-threshold kNN,we only use a small number of samples for training. We make three main contributions in this paper.1)We conduct an extensive experimental study to observe the finger's movement and the device's motion when performing a touch gesture,and then propose a spatial alignment method to align the touch gesture in space domain and segment the gesture into sub-gestures,to enhance the stability of the same user and the discriminability of different users.2)Based on a comprehensive analysis of touch sensor data and inertial sensor data,we propose a four-part feature selection method to represent the touch gestures with different topological structures in a uniform way,and select effective features based on the Fisher Score by considering both the intra-class stability and the inter-class discriminability.In addition,we also propose a multi-threshold KNN based model to mitigate the effect of different postures.3)We implement TouchID on an Android-powered smartphone and conduct a lot of experiments to evaluate the efficiency of TouchID Proc.ACM Interact.Mob.Wearable Ubiquitous Technol.,Vol.37,No.4,Article 162.Publication date:December 2020.TouchID: User Authentication on Mobile Devices via Inertial-Touch Gesture Analysis • 162:3 Touch sensor Successful authentication SETTING GESTURE CLASSIFIER TRANING TESTING Authentication failed SETTING GESTURE CLASSIFIER TRANING TESTING Inertial sensor Fig. 1. A typical application scenario of TouchID The first challenge is how to mitigate the intra-class difference and reduce the inter-class similarity among gestures? To address this challenge, we first conduct extensive experimental studies to observe how the finger moves in a touch gesture and how the device moves caused by the gesture. We find that time differences among touch gestures can affect intra-class difference and inter-class similarity. Therefore, we propose a spatial alignment method to align the sensor data in space domain, and then segment the touch gesture into multiple sub-gestures to highlight the sub-gesture which contributes more for enhancing the stability of the same user and the discriminability of different users. The second challenge is how to represent the touch gesture with different topological structures in a uniform way? The touch gestures corresponding to different graphic patterns have a different number of sub-gestures and the sub-gestures can also be different, which may lead to the different representation of a touch gesture. To address this challenge, we propose a four-part feature selection method, which classifies a touch gesture into four parts, i.e., a start node, an end node, turning node(s), and smooth paths, whatever the topological structure of the touch gesture is. Then, we select effective features for each part based on Fisher Score. Finally, for each gesture, we can represent it with a feature vector consisted of the uniform feature set. The third challenge is how to tolerate the uncertainty caused by different body postures and hand postures? When performing a touch gesture, the user can sit, lay or stand, and she/he can interact with the device with one hand or two hands, the different postures will lead to the inconsistency of the sensor data for the same touch gesture. To address this challenge, we design a multi-threshold KNN based model to separate the touch gestures under different postures into different clusters adaptively, and then perform user authentication in each cluster. In addition, to reduce the computation overhead of multi-threshold kNN, we only use a small number of samples for training. We make three main contributions in this paper. 1) We conduct an extensive experimental study to observe the finger’s movement and the device’s motion when performing a touch gesture, and then propose a spatial alignment method to align the touch gesture in space domain and segment the gesture into sub-gestures, to enhance the stability of the same user and the discriminability of different users. 2) Based on a comprehensive analysis of touch sensor data and inertial sensor data, we propose a four-part feature selection method to represent the touch gestures with different topological structures in a uniform way, and select effective features based on the Fisher Score by considering both the intra-class stability and the inter-class discriminability. In addition, we also propose a multi-threshold KNN based model to mitigate the effect of different postures. 3) We implement TouchID on an Android-powered smartphone and conduct a lot of experiments to evaluate the efficiency of TouchID. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 37, No. 4, Article 162. Publication date: December 2020