Learning Human Identity from Motion Patterns - Archive ouverte HAL Access content directly
Journal Articles IEEE Access Year : 2016

Learning Human Identity from Motion Patterns


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.
Fichier principal
Vignette du fichier
ieeeaccess2016.pdf (2.76 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01281946 , version 1 (21-09-2016)



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⟩
469 View
235 Download



Gmail Facebook X LinkedIn More