Gait recognition robust to speed transition using mutual subspace method

Yumi Iwashita, Hitoshi Sakano, Ryo Kurazume

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

11 Citations (Scopus)

Abstract

Person recognition from gait images is not robust to speed changes. To deal with this problem, generally existing methods have focused on training a model to transform gait features from various speeds into a common walking speed, and the model was trained with gait images with a variety of speeds. However in case that a subject walks with a speed which is not trained in the model, the performance gets worse. In this paper we introduce an idea that an image set-based matching approach, which omits walking speed information, has a potential to solve the problem. This is based on the assumption that speed information may not be critical information to gait recognition, since speed variations are universal phenomena. To prove the proposed idea, we apply a mutual subspace method to gait images and show the effectiveness of the proposed idea with the OU-ISIR gait speed transition database.

Original languageEnglish
Title of host publicationImage Analysis and Processing – ICIAP 2015 - 18th International Conference, Proceedings
EditorsVittorio Murino, Enrico Puppo, Vittorio Murino
PublisherSpringer Verlag
Pages141-149
Number of pages9
ISBN (Print)9783319232300
DOIs
Publication statusPublished - Jan 1 2015
Event18th International Conference on Image Analysis and Processing, ICIAP 2015 - Genoa, Italy
Duration: Sept 7 2015Sept 11 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9279
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Image Analysis and Processing, ICIAP 2015
Country/TerritoryItaly
CityGenoa
Period9/7/159/11/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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