Skip to Main content Skip to Navigation
New interface
Journal articles

Learning Human Identity from Motion Patterns

Abstract : We present a large-scale study, exploring the capability of temporal deep neural networks in interpreting 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.
Document type :
Journal articles
Complete list of metadata

Cited literature [31 references]  Display  Hide  Download
Contributor : Christian Wolf Connect in order to contact the contributor
Submitted on : Wednesday, September 21, 2016 - 3:41:26 PM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM
Long-term archiving on: : Thursday, December 22, 2016 - 1:32:33 PM


Files produced by the author(s)



Natalia Neverova, Christian Wolf, Lacey Griffin, Lex Fridman, Deepak Chandra, et al.. Learning Human Identity from Motion Patterns. IEEE Access, 2016, 4, pp.1810-1820. ⟨10.1109/ACCESS.2016.2557846⟩. ⟨hal-01281946⟩



Record views


Files downloads