Gait Energy Image Restoration Using Generative Adversarial Networks

Maryam Babaee, Yue Zhu, Okan Kopuklu, Stefan Hormann, Gerhard Rigoll

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

17 Scopus citations

Abstract

Gait is a biometric property that can be used for human identification in video surveillance. Basically, different gait features require motion of a person walking over one complete gait cycle. For example, in Gait Energy Image (GEI), average of silhouette images over one complete gait cycle is computed. However, in reality, there might be a partial gait cycle data available due to occlusion. In this paper, we propose a Generative Adversarial Network (GAN) in order to address the problem of gait recognition from incomplete gait cycle. Precisely, the network is able to reconstruct complete GEIs from incomplete GEIs. The proposed architecture is composed of (i) a generator which is an auto-encoder network to construct complete GEIs out of incomplete GEIs and (ii) two discriminators, one of which discriminates whether a given image is a full GEI while the other discriminates whether two GEIs belong to the same subject. We evaluate our approach on the OULP large gait dataset confirming that the proposed architecture successfully reconstructs complete GEIs from even extreme incomplete gait cycles.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages2596-2600
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 22 Sep 201925 Sep 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China
CityTaipei
Period22/09/1925/09/19

Keywords

  • Gait Energy Image
  • Gait Recognition
  • Generative Adversarial Networks

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