qinnzou/Gait-Recognition-Using-Smartphones
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
This project helps security professionals and biometric system designers identify or authenticate individuals based on their unique walking patterns, even when using everyday smartphones. It takes raw accelerometer and gyroscope data from a smartphone's inertial sensors as input and outputs a positive identification or authentication result for a person. This is ideal for anyone looking to implement unobtrusive person identification or verification using easily accessible mobile phone data.
125 stars. No commits in the last 6 months.
Use this if you need to identify or authenticate individuals based on their gait data collected informally via their smartphones in real-world, unconstrained settings.
Not ideal if you require traditional, highly controlled biometric data collection environments or need to identify individuals who are actively trying to conceal their gait.
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125
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49
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Jupyter Notebook
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Last pushed
Jan 31, 2024
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