  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 dataset of human movements,
passively collected by 1500 volunteers using their smartphones daily over
several months. We (1) compare several neural architectures for efficient
learning of temporal multi-modal data representations, (2) propose an optimized
shift-invariant dense convolutional mechanism (DCWRNN), and (3) 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.
