User Authentication Using Body Vibration Characteristics

Traditional mobile phone authentication systems are based on knowledge or biological information. This paper shows an authentication system based on human vibration characteristics and implement it on smartphone. This kind of inspiration comes from signal transmitted based on solid conduction and is applied to the link between vibration motor and accelerometer. Therefore, we designed a system to extract user biometric features by active vibration. However, there is a great challenge for smartphone systems with low sampling rate and requiring real-time response. Therefore, we would realize the system through multiple signal processing stages rather than choose a high-performance neural network. Our system solves the problem of low sampling rate of mobile phone sensors through supersampling reconstruction method. Besides, we select appropriate statistical features and MFCC-based features through PCA algorithm, and finally complete the training through Gradient Boosting Tree. In order to avoid the threshold division problem of the multi-level classifier, we train each sample in two classifications at the time of registration, and store the parameters in the user profile. When the system performs user authentication, the user data is divided into five sections for testing, so as to increase the robustness of the system. Our approach could achieve short-time identity authentication, with an average accuracy rate of 85.3%.