
Learning Human Identity from Motion Patterns - Oatseller
http://arxiv.org/abs/1511.03908
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Oatseller
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

