Human identification based on accelerometer sensors obtained by mobile phone data

In order to achieve secure usage digitally, many different methodologies (i.e., pin code, fingerprint, face recognition) have been employed. In this study, a novel way of user identification, which can be expressed as a biometrical method, has been proposed. The proposed approach was based on the characteristics of mobile phone usage (position changes in carrying, talking, and other actions). To assess and validate the proposed method, a dataset, which consists of millions of data collected from users with the help of accelerometers for several months during their ordinary smartphone usage, was obtained. This large dataset was reduced by randomly taking 3000 samples from each of the 387 devices in the dataset. The arbitrarily selected signals were labeled according to "one against all" (or "one vs. all") strategies. Extracted features were classified by the k nearest neighbor (kNN) and the randomized neural network (RNN), machine learning methods. It has been seen that behavior-based biometric recognition can be accomplished with mobile phone accelerometer data, with 99.994% success rates for kNN and 99.97% for RNN.