Abstract
This paper proposed a novel gait recognition method that is based on plantar pressure images. Different from many conventional methods where hand-crafted features are extracted explicitly. We utilized Convolution Neural Network (CNN) for automatic feature extraction as well as classification. The peak pressure image (PPI) generated from the time series of plantar pressure images is used as the characteristic image for gait recognition in this study. Our gait samples are collected from 109 subjects under three kinds of walking speeds, and for each subject total 18 samples are gathered. Experimental results demonstrate that the designed CNN model can obtain very high classification accuracy as compared to many traditional methods.
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Acknowledgments
This work is supported by Anhui Provincial Natural Science Foundation (grant number 1608085MF136); China Postdoctoral Science Foundation (2015M582826); Major University Science Research Project of Anhui Province (grant number KJ2016SD33); Anhui Province Science and Technology Major Project (grant number 1603081122); National Natural Science Foundation of China (NSFC) for Youth (grant numbers 61402004, 61602002).
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Li, Y. et al. (2017). A Convolutional Neural Network for Gait Recognition Based on Plantar Pressure Images. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_50
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DOI: https://doi.org/10.1007/978-3-319-69923-3_50
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