Human gait seamless continuous authentication, based on wearable accelerometers, is a novel biometric instrument which can be exploited to identify the user of mobile and wearable devices. In this paper, we present a study on recognition of user identity, by analysis of gait data, collected through body inertial sensors from 175 different users. The mechanism used for identity recognition is based on deep learning machinery, specically on a convolutional network, trained with readings from dierent sensors, and on filtering and buffering mechanism to increase the accuracy. Results show a very high accuracy in both recognizing known and unknown identities.