Evaluating a New Conversive Hidden non-Markovian Model Approach for Online Movement Trajectory Verification

Tim Dittmar, Claudia Krull, Graham Horton

Abstract

This paper presents further research on an implemented classification and verification system that employs a novel approach for stochastically modelling movement trajectories. The models are based on Conversive Hidden non-Markovian Models that are especially suited to mimic temporal dynamics of time series as in contrast to the relative Hidden Markov Models(HMM) and the dynamic time warping(DTW) method, timestamp information of data are an integral part. The system is able to create trajectory models from examples and is tested on signatures, doodles and pseudo-signatures for its verification performance. By using publicly available databases comparisons are made to evaluate the potential of the system. The results reveal that the system already performs similar to a general DTW approach on doodles and pseudo-signatures but does not reach the performance of specialized HMM systems for signatures. But further possibilities to improve the results are discussed.

References

  1. Bosse, S., Krull, C., and Horton, G. (2011). Modeling of gestures with differing execution speeds: are hidden non-markovian models applicable for gesture recognition. In MAS: The International Conference on Modelling & Applied Simulation, pages 189-194.
  2. Buchholz, R. (2012). Conversive Hidden non-Markovian Models. PhD thesis, Otto-von-Guericke-Universitt Magdeburg.
  3. Dittmar, T., Krull, C., and Horton, G. (2013). Using conversive hidden non-markovian models for multi-touch gesture recognition. In The 12th International Conference on Modeling & Applied Simulation.
  4. Dittmar, T., Krull, C., and Horton, G. (2015). An improved conversive hidden non-markovian model-based touch gesture recognition system with automatic model creation. In The 14th International Conference on Modeling & Applied Simulation.
  5. Faundez-Zanuy, M. (2007). On-line signature recognition based on vq-dtw. Pattern Recognition, 40(3):981-992.
  6. Fierrez, J., Ortega-Garcia, J., Ramos, D., and GonzalezRodriguez, J. (2007). Hmm-based on-line signature verification: Feature extraction and signature modeling. Pattern recognition letters, 28(16):2325-2334.
  7. Fink, G. A. (2014). Markov models for pattern recognition: from theory to applications. Springer Science & Business Media.
  8. Kholmatov, A. and Yanikoglu, B. (2005). Identity authentication using improved online signature verification method. Pattern recognition letters, 26(15):2400- 2408.
  9. Kholmatov, A. and Yanikoglu, B. (2009). Susig: an on-line signature database, associated protocols and benchmark results. Pattern Analysis and Applications, 12(3):227-236.
  10. Martinez-Diaz, M., Fierrez, J., and Galbally, J. (2013). The doodb graphical password database: data analysis and benchmark results. IEEE Access, 1:596-605.
  11. Martinez-Diaz, M., Fierrez, J., Krish, R. P., and Galbally, J. (2014). Mobile signature verification: feature robustness and performance comparison. IET Biometrics, 3(4):267-277.
  12. Muramatsu, D. and Matsumoto, T. (2003). An hmm online signature verifier incorporating signature trajectories. In Document Analysis and Recognition, 2003. Proceedings. Seventh International Conference on, pages 438-442. IEEE.
  13. Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., et al. (2003). Mcyt baseline corpus: a bimodal biometric database. IEE Proceedings-Vision, Image and Signal Processing, 150(6):395-401.
  14. Rabiner, L. and Juang, B. (1986). An introduction to hidden markov models. ieee assp magazine, 3(1):4-16.
Download


Paper Citation


in Harvard Style

Dittmar T., Krull C. and Horton G. (2017). Evaluating a New Conversive Hidden non-Markovian Model Approach for Online Movement Trajectory Verification . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 249-258. DOI: 10.5220/0006212502490258


in Bibtex Style

@conference{icpram17,
author={Tim Dittmar and Claudia Krull and Graham Horton},
title={Evaluating a New Conversive Hidden non-Markovian Model Approach for Online Movement Trajectory Verification},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={249-258},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006212502490258},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Evaluating a New Conversive Hidden non-Markovian Model Approach for Online Movement Trajectory Verification
SN - 978-989-758-222-6
AU - Dittmar T.
AU - Krull C.
AU - Horton G.
PY - 2017
SP - 249
EP - 258
DO - 10.5220/0006212502490258