View invariant gait recognition using only one uniform model

Shiqi Yu, Qing Wang, Linlin Shen, Yongzhen Huang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Gait recognition has been proved useful in human identification at a distance. But view variance of gait feature is always a great challenge because of the difference in appearance. If the view of the probe is different from that of the gallery, one view transformation model can be employed to convert the gait feature from one view to another. But most existing models need to estimate the view angle first, and can work for only one view pair. They can not convert multi-view data to one specific view efficiently. We employ one deep model based on auto-encoder for view invariant gait extraction. The model can synthesize gait feature in a progressive way by stacked multi-layer auto-encoders. The unique advantage is that it can extract view invariant feature from any view using only one model, and view estimation is not needed. The proposed method is evaluated on a large dataset, CASIA Gait Dataset B. The experimental results show that it can achieve state-of-the-art performance, and the improvement is more obvious when the view variance is larger.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages889-894
Number of pages6
ISBN (Electronic)9781509048472
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume0
ISSN (Print)1051-4651

Conference

Conference23rd International Conference on Pattern Recognition, ICPR 2016
Country/TerritoryMexico
CityCancun
Period4/12/168/12/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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