Gait-based person identification method using shadow biometrics for robustness to changes in the walking direction

Makoto Shinzaki, Yumi Iwashita, Ryo Kurazume, Koichi Ogawara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

Person recognition from gait images is generally not robust to changes in appearance, such as variations of the walking direction. In general conventional methods have focused on training a model to transform gait features or gait images to those at a different viewpoint, but the performance gets worse in case the model is not trained at a viewpoint of a subject. In this paper we propose a novel gait recognition approach which differs a lot from existing approaches in that the subject's sequential 3D models and his/her motion are directly reconstructed from captured images, and arbitrary viewpoint images are synthesized from the reconstructed 3D models for the purpose of gait recognition robust to changes in the walking direction. Moreover, we propose a gait feature, named Frame Difference Frieze Pattern (FDFP), which is robust to high frequency noise. The efficiency of the proposed method is demonstrated through experiments using a database that includes 41 subjects.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages670-677
Number of pages8
ISBN (Electronic)9781479966820
DOIs
Publication statusPublished - Feb 19 2015
Event2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, United States
Duration: Jan 5 2015Jan 9 2015

Publication series

NameProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015

Other

Other2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
Country/TerritoryUnited States
CityWaikoloa
Period1/5/151/9/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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