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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

DeepGait: A Learning Deep Convolutional Representation for Gait Recognition

verfasst von : Xianfu Zhang, Shouqian Sun, Chao Li, Xiangyu Zhao, Yuping Hu

Erschienen in: Biometric Recognition

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Gesture Recognition Based on Deep Belief Networks
Nächstes Kapitel Gait Identification by Joint Spatial-Temporal Feature
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Metadaten
Titel
DeepGait: A Learning Deep Convolutional Representation for Gait Recognition
verfasst von
Xianfu Zhang
Shouqian Sun
Chao Li
Xiangyu Zhao
Yuping Hu
Copyright-Jahr
2017
Buch
Biometric Recognition

Print ISBN: 978-3-319-69922-6

Electronic ISBN: 978-3-319-69923-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-69923-3_48

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