Learning Human Identity From Motion Patterns

We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. At Google, we have created a first-of-its-kind data set of human movements, passively collected by 1500 volunteers using their smartphones daily over several months. We compare several neural architectures for efficient learning of temporal multi-modal data representations, propose an optimized shift-invariant dense convolutional mechanism, and incorporate the discriminatively trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems. Finally, we demonstrate that the proposed model can also be successfully applied in a visual context.

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