Inertial Data Based Learning Methods for Person Authentication

A user authentication method based on biometric movement patterns is proposed. As input, the data acquired from the accelerometer of a wearable device is used. Using machine learning methods, i.e. k-Nearest Neighbors, Random Forests classifiers and a ID Convolutional Neural Network, the person which performs a known activity is identified from a set of 15 persons. Most approaches in the domain propose either the human activity type recognition or person identification using the gait pattern. In this research, the considered activities are: Walking and Working at computer. As features, sequences of 52 consecutive accelerations on the three axes are selected using the overlapping time window method. Even if computer work is a mainly static activity, the results obtained for identifying the person performing it are encouraging. The main advantage of the proposed method is that it does not involve computing of other features than those from the acceleration data.