A Sliding Window Based Voting Classifier for Activity Sensor Based User Identification



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Identification is the core of any authentication protocol design as the purpose of the authentication is to verify the user’s identity. The efficient establishment and verification of identity remain a big challenge. Recently, biometrics-based identification algorithms gained popularity as a means of identifying individuals using their unique biological characteristics. In this thesis, we propose a novel and efficient identification framework, ActID, which can identify a user based on his/her hand motion while walking. ActID not only selects a set of high-quality features based on Optimal Feature Evaluation and Selection and Correlation-based Feature Selection algorithms but also includes a novel sliding window based voting classifier. Therefore, it achieves several important design goals for gait authentication based on resource-constrained devices, including lightweight and real-time classification, high identification accuracy, a minimum number of sensors, and a minimum amount of data collected. Performance evaluation shows that ActID is cost-effective and easily deployable, selects only a minimum number of 10 high-quality features, uses only accelerometer sensor and increases the cost efficiency of user identification, collects only a small amount of 10 seconds of activity data, satisfies real-time requirements, and achieves a high identification accuracy of 100% when applied to a 30 user dataset.



wearables, sensors, biometric, feature evaluation, feature selection, identification, classification