Biometrics have become important for authentication on
		  mobile devices, e.g. to unlock devices before using them.
		  One way to protect biometric information stored on mobile
		  devices from disclosure is using embedded smart cards (SCs)
		  with biometric match-on-card (MOC) approaches. Com-
		  putational restrictions of SCs thereby also limit biometric
		  matching procedures. We present a mobile MOC approach that
		  uses offline training to obtain authentication models with
		  a simplistic internal representation in the final trained
		  state, whereat we adapt features and model representation
		  to enable their usage on SCs. The obtained model is used
		  within SCs on mobile devices without requiring retraining
		  when enrolling individual users. We apply our approach to
		  acceleration based mobile gait authentication, using a 16
		  bit integer range Java Card, and evaluate authentication
		  performance and computation time on the SC using a pub-
		  licly available dataset. Results indicate that our approach
		  is feasible with an equal error rate of ∼12% and a
		  computation time below 2s on the SC, including data
		  transmissions and computations. To the best of our
		  knowledge, this thereby represents the first practically
		  feasible approach towards acceleration based gait
		  match-on-card authentication.<br>
<br><pre>@inproceedings{Findling_16_MobileGaitMatch,
  author = {Findling, Rainhard Dieter and H\"olzl, Michael and Mayrhofer, Ren\'e},
  title = {Mobile Gait Match-on-Card Authentication from Acceleration
  		  Data with Offline-Simplified Models},
  booktitle = {Proc. {MoMM} 2016: 14th International Conference on
  		  Advances in Mobile Computing and Multimedia},
  year = {2016},
  pages = {250--260},
  address = {Singapore},
  month = nov,
  publisher = {ACM},
  doi = {10.1145/3007120.3007132},
  keywords = {Acceleration; authentication; gait; match-on-card; mobile
  		  biometrics; smart card;},
  url = {https://dl.acm.org/citation.cfm?id=3007132}
}
</pre>