Gait recognition with multi-region size convolutional neural network for authentication with wearable sensors

Abstract

As inertial sensors are low-cost, easy-to-use, and can be integrated in wearable devices, they can be used to establish as a new modality for user authentication in the smart environment in which computing systems can recognize persons implicitly by their walking patterns. This motivates our proposal to use multi-region size Convolutional Neural Network to recognize users from their gait patterns recorded from accelerometers and gyroscopes in mobile and wearable devices. Experiments on Inertial Sensor Dataset of OU-ISIR Gait Database, the largest inertial sensor-based gait database, demonstrate that our best CNN models provide the accuracy of 96.84 % and EER of 10.43 %, better than those of existing methods. Furthermore, we also prove by experiments that by using only a subset of subjects in OU-ISIR dataset to train CNN models, our method can achieve the accuracy and EER approximately (95.53 ± 0.82) % and (11.60 ± 0.98) %, respectively.