Evaluation of Algorithms for Orientation Invariant Inertial Gait Matching

With the prevalent use of smart phones in sensitive applications, unobtrusive methods for continuously verifying the identity of the user have become critical. The embedded inertial sensors in these devices provide an opportunity to develop authentication processes based on behavioral biometrics such as gait. However, one major obstacle is that the orientation of the device relative to the user is hard to control and difficult to determine reliably. This paper presents five methods: magnitude (MAG), principal component analysis (PCA), vector cross product (VCP), reduced gait dynamics image (rGDI), and Kabsch alignment (KAB) that make the authentication process independent of device orientation and hence improve the performance. The five methods are evaluated and compared on two large, publicly available, inertial gait datasets. The baseline (orientation dependent) average equal error rate (EER) when the device was freely oriented is 26.4%. The MAG, PCA, VCP, and rGDI methods reduce the average EER to approximately 23%. The Kabsch (KAB) method is more effective and reduces the average EER to 20.2%.