Making gait recognition robust to speed changes using mutual subspace method

Yumi Iwashita, Mafune Kakeshita, Hitoshi Sakano, Ryo Kurazume

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

4 Citations (Scopus)

Abstract

Mutual subspace method (MSM), which is one of image-based approaches, showed strong discrimination capability in gait recognition. In general, 2D image matrices are transformed into 1D image vectors to be used as input into MSM, and then principal component analysis (PCA) is applied to 1D vectors to generate a subspace. However, due to the high dimensionalities of 1D vectors, the evaluation accuracy of the covariance matrix in PCA is not high enough. This results in a decrease in performance, especially in case that speed difference between gallery and probe dataset is big. Thus in this paper we propose a method, which expands the MSM-based method, to recognize people with higher accuracy. The proposed method divides the human body area into multiple areas, followed by adaptive choice of areas that have high discrimination capability. Moreover, the proposed method utilizes the frieze pattern, which is one of gait features, as an additional input into MSM. The use of divided areas and the frieze pattern allows us to evaluate the covariance matrix with higher accuracy. In experiments we applied the proposed method to challenging databases with speed variations, and we show the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2273-2278
Number of pages6
ISBN (Electronic)9781509046331
DOIs
Publication statusPublished - Jul 21 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: May 29 2017Jun 3 2017

Other

Other2017 IEEE International Conference on Robotics and Automation, ICRA 2017
CountrySingapore
CitySingapore
Period5/29/176/3/17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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