Gait2Vec: Continuous Authentication of Smartphone Users Based on Gait Behavior

Since gait is hard to imitate and can be easily collected by smartphone inertial sensors, it can be applied to user authentication. Traditional neural network based methods tend to train feature extractor and classifier together for each user in user authentication. These methods are difficult to guarantee that the feature extractor designed for specific users is suitable for other users. The accuracy is low when the sample size of a legitimate user is small, and the time overhead is heavy when the total amount of legitimate users is large. Besides, there are often strong constraints on sensor position, walking route, walking speed and external scenario when collecting gait data. In this paper, in order to reduce the cost of time and improve the robustness of the model, we use the idea of transfer learning to design our Gait2Vec feature extractor. It is pre-trained in the user identification task and then transferred to user authentication task for feature extraction. Meanwhile, a gait dataset of 21 subjects is collected under weak constraints in 2 scenarios for experimental purposes. Extensive analysis demonstrates that our models achieve a high performance with the accuracy over 94% in user identification and 97% in user authentication.