Learning Human Identity from Motion Patterns
This work addresses the need for active biometric authentication systems using mobile sensors, offering a novel component for multi-modal security applications.
The study tackled the problem of using human motion patterns for biometric authentication by developing a method that leverages temporal deep neural networks on a large-scale dataset of smartphone-collected movements, achieving results that demonstrate human kinematics can effectively convey user identity.
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.