Improving gait recognition with 3D pose estimation

Weizhi An, Rijun Liao, Shiqi Yu*, Yongzhen Huang, Pong Chi YUEN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

37 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationBiometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings
EditorsZhenan Sun, Shiguang Shan, Zhenhong Jia, Kurban Ubul, Jie Zhou, Jianjiang Feng, Zhenhua Guo, Yunhong Wang
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319979083
Publication statusPublished - 2018
Event13th Chinese Conference on Biometric Recognition, CCBR 2018 - Urumchi, China
Duration: 11 Aug 201812 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10996 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th Chinese Conference on Biometric Recognition, CCBR 2018

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • 3D pose
  • CNNs
  • Gait recognition
  • LSTM


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