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Walk to Show Your Identity: Gait-based Seamless User Authentication Framework Using Deep Neural Network

This is python code for the paper work published in WearSys '19: The 5th ACM Workshop on Wearable Systems and Applications. You can access to the paper through this link

Prerequisities

  • Language: Python
  • Required Packages: numpy, pandas, matplotlib, scipy, sklearn, tensorflow
  • To install the required package, type the following command (To install Tensorflow, visit the official Tensorflow Webpage link):
  1. Python 2
pip install numpy pandas matplotlib scipy sklearn
  1. Python 3
pip3 install numpy pandas matplotlib scipy sklearn

Running the code

  1. Data Filtering & Visualization of Frequency Distribution
python3 data_filter_fft.py
python3 valid_start_end.py

Data Filter

  1. Gait Cycle Detection: slice walk cycles from the data sequences
python3 cycle_detection.py

Interpolation

  1. Interpolation: make walk cycles consistent in length
python3 interpolation.py

Interpolation

  1. Cycle Filtering: filter out noisy cycles
python3 cycle_filter.py

Cycle Filter

  1. Classification: DNN (Multi Layer Perceptron), CNN, and RNN (LSTM)
python3 DNN.py
python3 CNN.py
python3 RNN.py

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