Hand-Interactive Behavior Analysis for User Authentication Systems with Wrist-Worn Devices

The growing trend of using wearable devices for context-aware computing and pervasive sensing systems has raised its potentials for quick and reliable authentication techniques. We collect users' writing actions with their wrist-worn devices and discover an appealing observation: the writing pattern of a person is kind of unique, stable and distinguishable. This paper presents a novel user authentication system through wrist-worn devices by analyzing the interaction behavior with users, which is both accurate and efficient for future usage. The key feature of our approach lies in using Savitzky-Golay filter and Dynamic-Time-Warping method to obtain fine-grained writing metrics for user authentication. These new metrics are relatively unique from person to person and independent of the computing platform. Analyses are conducted on the wristband-interaction data with diversity in gender, age, and height of the users. Extensive experimental results show that the proposed approach can identify users in a timely and accurate manner, with a false negative rate of 1.78%, false positive rate of 6.7%, and Area Under ROC Curve of 0.983. Additional examinations on robustness to various mimic attacks, tolerance to abnormal training data, and comparisons are provided to further analyze the applicability.