Abstract
Human gait, as a soft biometric, helps to recognize people by walking. To further improve the recognition performances, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features. DeepGait is generated by using an pre-trained VGG-D net without any fine-tuning. When compared with other traditional hand-crafted gait representations (eg. GEI, FDF and GFI etc.) experimentally on OU-ISR large population (OULP) dataset and CASIA-B dataset, DeepGait has been shown that the performances of the proposed method is outstanding under different walking variations (view, clothing, carrying bags). The OULP dataset, which includes 4007 subjects, makes our result reliable in a statically way. Even in a very low dimension, our proposed gait representation still outperforms the commonly used 11264-dimensional GEI. For further comparison, all the gait representation vectors are available.
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Acknowledgments
The authors would like to thank OU-ISIR and CBSR for providing access to the OU-ISIR Large Population Gait Database and CASIA-B Gait Database. This study is partly supported by the National Natural Science Foundation of China (No. 61562072).
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Zhang, X., Sun, S., Li, C., Zhao, X., Hu, Y. (2017). DeepGait: A Learning Deep Convolutional Representation for Gait Recognition. 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_48
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DOI: https://doi.org/10.1007/978-3-319-69923-3_48
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