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Gait Lateral Network: Learning Discriminative and Compact Representations for Gait Recognition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12354)

Abstract

Gait recognition aims at identifying different people by the walking patterns, which can be conducted at a long distance without the cooperation of subjects. A key challenge for gait recognition is to learn representations from the silhouettes that are invariant to the factors such as clothing, carrying conditions and camera viewpoints. Besides being discriminative for identification, the gait representations should also be compact for storage to keep millions of subjects registered in the gallery. In this work, we propose a novel network named Gait Lateral Network (GLN) which can learn both discriminative and compact representations from the silhouettes for gait recognition. Specifically, GLN leverages the inherent feature pyramid in deep convolutional neural networks to enhance the gait representations. The silhouette-level and set-level features extracted by different stages are merged with the lateral connections in a top-down manner. Besides, GLN is equipped with a Compact Block which can significantly reduce the dimension of the gait representations without hindering the accuracy. Extensive experiments on CASIA-B and OUMVLP show that GLN can achieve state-of-the-art performance using the 256-dimensional representations. Under the most challenging condition of walking in different clothes on CASIA-B, our method improves the rank-1 accuracy by \(6.45\%\).

Keywords

Gait recognition Lateral connections Discriminative representations Compact representations 

Notes

Acknowledgements

We are grateful to Prof. Dongbin Zhao for his support to this work.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Beijing University of TechnologyBeijingChina
  3. 3.WATRIX AIBeijingChina

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