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Improving Gait Recognition with 3D Pose Estimation

  • Weizhi An
  • Rijun Liao
  • Shiqi Yu
  • Yongzhen Huang
  • Pong C. Yuen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

Gait is a kind of attractive biometric feature for human identification in recent decades. The view, clothing, carrying and other variations are always the challenges for gait recognition. One of the possible solutions is the model based methods. In this paper, 3D pose is estimated from 2D images are used as the feature for gait recognition. So gait can be described by the motion of human body joints. Besides, the 3D pose has better capacity for view variation than the 2D pose. Experimental results also prove that in the paper. To improve the recognition rates, LSTM and CNNs are employed to extract temporal and spatial features. Compared with other model-based methods, the proposed one has achieved much better performance and is comparable with appearance-based ones. The experimental results show the proposed 3D pose based method has unique advantages in large view variation. It will have great potential with the development of pose estimation in future.

Keywords

Gait recognition 3D pose LSTM CNNs 

Notes

Acknowledgment

The work is supported by the strategic new and future industrial development fund of Shenzhen (Grant No. 20170504160426188).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Weizhi An
    • 1
  • Rijun Liao
    • 1
  • Shiqi Yu
    • 1
  • Yongzhen Huang
    • 2
  • Pong C. Yuen
    • 3
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.Department of Computer ScienceHong Kong Baptist UniversityHong Kong SARChina

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