tl;dr 8. Conclusion From a modeling perspective, this work has demon- strated that temporal architectures are particularly efficient for learni

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