On the Instability of Sensor Orientation in Gait Verification on Mobile Phone

Thang Hoang, Deokjai Choi, Thuc Nguyen

2015

Abstract

Authentication schemes using tokens or biometric modalities have been proposed to ameliorate the security strength on mobile devices. However, the existing approaches are obtrusive since the user is required to perform explicit gestures in order to be authenticated. While the gait signal captured by inertial sensors is understood to be a reliable profile for effective implicit authentication, recent studies have been conducted in ideal conditions and might therefore be inapplicable in the real mobile context. Particularly, the acquiring sensor is always fixed to a specific position and orientation. This paper mainly focuses on addressing the instability of sensor’s orientation which mostly happens in the reality. A flexible solution taking advantages of available sensors on mobile devices which can help to handle this problem is presented. Moreover, a novel gait recognition method utilizes statistical analysis and supervised learning to adapt itself to the instability of the biometric gait under various circumstances is also proposed. By adopting PCA+SVM to construct the gait model, the proposed method outperformed other state-of-the-art studies, with an equal error rate of 2.45% and accuracy rate of 99.14% in terms of the verification and identification aspects being achieved, respectively.

References

  1. Ailisto, H. (2005). Identifying people from gait pattern with accelerometers. In InDefense and Security. SPIE.
  2. Breitinger, F. and Nickel, C. (2010). User survey on phone security and usage. In BIOSIG. GI.
  3. Chang, C. and Lin, C. (2011). Libsvm: a library for support vector machines. In ACM Transactions on Intelligent Systems and Technology (TIST). ACM.
  4. Daubechies, I. (1992). Ten lectures on wavelets. In Philadelphia: Society for industrial and applied mathematics. Society for Industrial and Applied Mathematics.
  5. Derawi, M. and Bours, P. (2013). Gait and activity recognition using commercial phones. In Computers & Security. Elsevier Advanced Technology Publications.
  6. Derawi, M. et al. (2010a). Improved cycle detection for accelerometer based gait authentication. In In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE.
  7. Derawi, M. et al. (2010b). Unobtrusive user-authentication on mobile phones using biometric gait recognition. In In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2010 Sixth International Conference on. IEEE.
  8. Fish, D. and Nielsen, J. (1993). Clinical assessment of human gait. In Journal of Prosthetics and Orthotics. JPO.
  9. Frank, J. et al. (2010). Activity and gait recognition with time-delay embeddings. In AAAI. AAAI Press.
  10. Gafurov, D. et al. (2010). Improved gait recognition performance using cycle matching. In Advanced Information Networking and Applications Workshops (WAINA), 2010 IEEE 24th International Conference on. IEEE.
  11. Gafurov, D. and Snekkenes, E. (2009). Gait recognition using wearable motion recording sensors. In EURASIP Journal on Advances in Signal Processing. Hindawi Publishing Corp.
  12. Hoang, T. et al. (2013). A Lightweight Gait Authentication on Mobile Phone Regardless of Installation Error. Springer, Berlin.
  13. Holien, K. (2008). Gait recognition under non-standard circumstances.
  14. Jain, A. et al. (2004). An introduction to biometric recognition. In Circuits and Systems for Video Technology, IEEE Transactions on. IEEE.
  15. Li, Y. et al. (2011). Gait authentication based on acceleration signals of ankle. In Chinese Journal of Electronics.
  16. Lu, H. et al. (2014). Unobtrusive gait verification for mobile phones. In Proceedings of the 2014 ACM International Symposium on Wearable Computers. ACM.
  17. Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation. In Pattern Analysis and Machine Intelligence, IEEE Transactions on. IEEE.
  18. Mjaaland, B. et al. (2011). Paper templates. In Walk the walk: attacking gait biometrics by imitation. SPRINGER.
  19. Mondal, S. et al. (2012). Gait based personal identification system using rotation sensor. In Journal of Emerging Trends in Computing and Information Sciences.
  20. Ngo, T. et al. (2014). In intelligent information hiding and multimedia signal processing (iih-msp). In Pattern Recognition. Elsevier Science Inc.
  21. Pan, G. et al. (2009). Accelerometer-based gait recognition via voting by signature points. In Electronics letters.
  22. Rong, L. et al. (2007). A wearable acceleration sensor system for gait recognition. In Industrial Electronics and Applications, 2007. ICIEA 2007. 2nd IEEE Conference on. IEEE.
  23. Sprager, S. and Zazula, D. (2009). A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine. In WSEAS Transactions on Signal Processing. World Scientific and Engineering Academy and Society (WSEAS).
  24. Terada, S. et al. (2011). Performance of gait authentication using an acceleration sensor. In Telecommunications and Signal Processing (TSP). IEEE.
  25. Whitle, M. (2003). Gait analysis: an introduction.
Download


Paper Citation


in Harvard Style

Hoang T., Choi D. and Nguyen T. (2015). On the Instability of Sensor Orientation in Gait Verification on Mobile Phone . In Proceedings of the 12th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2015) ISBN 978-989-758-117-5, pages 148-159. DOI: 10.5220/0005572001480159


in Bibtex Style

@conference{secrypt15,
author={Thang Hoang and Deokjai Choi and Thuc Nguyen},
title={On the Instability of Sensor Orientation in Gait Verification on Mobile Phone},
booktitle={Proceedings of the 12th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2015)},
year={2015},
pages={148-159},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005572001480159},
isbn={978-989-758-117-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Security and Cryptography - Volume 1: SECRYPT, (ICETE 2015)
TI - On the Instability of Sensor Orientation in Gait Verification on Mobile Phone
SN - 978-989-758-117-5
AU - Hoang T.
AU - Choi D.
AU - Nguyen T.
PY - 2015
SP - 148
EP - 159
DO - 10.5220/0005572001480159