Learning Human Identity from Motion Patterns

tl;dr

   8. Conclusion

    From a modeling perspective, this work has demon-
    strated that temporal architectures are particularly efficient
    for learning of dynamic features from a large corpus of
    noisy temporal signals, and that the learned representations
    can be further incorporated in a generative setting. With
    respect to the particular application, we have confirmed
    that natural human kinematics convey necessary informa-
    tion about person identity and therefore can be useful for
    user authentication on mobile devices. The obtained results
    look particularly promising, given the fact that the system is
    completely non-intrusive and non-cooperative,i.e. does not
    require any effort from the user’s side.

    Non-standard weak biometrics are particularly interest-
    ing for providing the context in, for example, face recog-
    nition or speaker verification scenarios. Further augmenta-
    tion with data extracted from keystroke and touch patterns,
    user location, connectivity and application statistics (ongo-
    ing work) may be a key to creating the first secure non-
    obtrusive mobile authentication framework