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)


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.


Gait classification Wearable devices Gait labeling Convolutional neural networks Time series 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

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

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