Advertisement

Gait Classification and Identity Authentication Using CNN

  • Wei Yuan
  • Linxuan ZhangEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)

Abstract

Mobile-securance is one of the most crucial concerns in the contemporary society. To make further supplementation on the security of mobile phones, this paper proposes a sequential method including periodogram based gait separation, convolutional neural network (CNN) based gait classification and authentication algorithm. The implementation has also been achieved in this paper. The original data are obtained from mobile phone built-in accelerometer. Periodogram based gait separation algorithm calculates the walking periodicity of the mobile phone users and separates individual gait from the time series. Using CNN based classification whose overall classification accuracy can reach over 90% in the test, the separated gaits are subsequently categorized into 6 gait patterns. Furthermore, the CNN based identification authentication convert the certification issue to a bi-section issue, whether the mobile phone holder is the mobile phone user or not. The CNN based authentication method may achieve an accuracy of over 87% when combing with the walking periodicity data of mobile phone users. Albeit the high overall accuracy of CNN based classification and identification authentication, currently the method still have potential deficiency which requires further researches, preparing for public application and popularization.

Keywords

Gait classification Wearable devices Gait labeling Convolutional neural networks Time series 

References

  1. 1.
    Ailisto, H.J., Makela, S.M.: Identifying people from gait pattern with accelerometers. In: Biometric Technology for Human Identification II, pp. 7–14 (2005)Google Scholar
  2. 2.
    Zdragkas, G., Avaritsiotis, J.N.: Gait analysis and automatic gait event identification using accelerometers. In: IEEE International Conference on Bioinformatics and Bioengineering, Bibe 2008, 8–10 October 2008, Athens, Greece, pp. 1–6. DBLP (2008)Google Scholar
  3. 3.
    Lin, J., Chan, L., Yan, H.: A decision tree based pedometer and its implementation on the android platform. In: International Conference on Computer Science and Information Technology, pp. 73–83 (2015)Google Scholar
  4. 4.
    Zhao, Y., Zhou, S.: Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network. Sensors 17(3), 478 (2017)CrossRefGoogle Scholar
  5. 5.
    Świtoński, A., Michalczuk A., Josiński H., et al.: Dynamic time warping in gait classification of motion capture data. In: World Academy of Science Engineering Technology (2012)Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Subramanian, R., Sarkar, S., Labrador, M., et al.: Orientation invariant gait matching algorithm based on the Kabsch alignment. In: IEEE International Conference on Identity, Security and Behavior Analysis. IEEE (2015)Google Scholar
  9. 9.
    Farah, J.D., Baddour, N., Lemaire, E.D.: Gait phase detection from thigh kinematics using machine learning techniques. In: IEEE International Symposium on Medical Measurements and Applications, pp. 263–268. IEEE (2017)Google Scholar
  10. 10.
    Chuang, A.: Time series analysis: univariate and multivariate methods. Technometrics 33(1), 108–109 (2006)CrossRefGoogle Scholar
  11. 11.
    Mehrgardt, S.: Median filter: US, US5138567[P] (1992)Google Scholar
  12. 12.
    Kurz, M.J., Stergiou, N.: An artificial neural network that utilizes hip joint actuations to control bifurcations and chaos in a passive dynamic bipedal walking model. Biol. Cybern. 93(3), 213–221 (2005)CrossRefGoogle Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)Google Scholar
  14. 14.
    Murray, N., Perronnin, F.: Generalized max pooling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2473–2480. IEEE Computer Society (2014)Google Scholar
  15. 15.
    Kearas. https://keras.io/. Accessed 7 June 2018

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.University of Science and Technology BeijingBeijingChina
  2. 2.Tsinghua UniversityBeijingChina

Personalised recommendations