Human authentication from ankle motion data using convolutional neural networks

We present a data acquisition and signal processing framework for the authentication of users from their gait signatures (accelerometer and gyroscope data). An ankle-worn inertial measurement unit (IMU) is utilized to acquire the raw motion data, which is pre-processed and used to train a number of signal processing tools, including a convolutional neural network (CNN) for the extraction of features as well as one-class single- and multi-stage classifiers. The CNN is trained (offline and only once) using a representative set of subjects and is then exploited as a universal feature extractor, i.e., to extract relevant features from walking patterns of previously unseen subjects. The one-class classifier is instead solely trained on the subject that we intend to authenticate (the target user). Scores from the one-class classifier are finally fed into a multi-stage decision maker, which performs a sequential decision testing for improved accuracy. The system operates in an online fashion, delivering excellent results, while requiring in the worst case fewer than five walking cycles to reliably authenticate the user.