Gait recognition using binarized statistical image features and histograms of oriented gradients

Jashila Nair Mogan, Chin Poo Lee, Kian Ming Lim, Alan W.C. Tan

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

24 Citations (Scopus)

Abstract

This paper presents a gait recognition method using the combination of motion history image (MHI), binarized statistical image features (BSIF) and histograms of oriented gradients (HOG). The method first encodes the motion pattern and direction of the gait cycle in motion history image. Subsequently, performing convolution on the motion history image using pre-learnt filters as kernel, binarized statistical image features are generated by summing the convolution output images. Histograms of oriented gradients are then computed on binarized statistical image features. Gait signature of a gait cycle is attained by accumulating all the HOG descriptors. Experimental result shows that the proposed method performs promisingly in gait recognition.

Original languageEnglish
Title of host publicationProceeding of 2017 International Conference on Robotics, Automation and Sciences, ICORAS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538619087
DOIs
Publication statusPublished - 2 Jul 2017
Externally publishedYes
Event2017 International Conference on Robotics, Automation and Sciences, ICORAS 2017 - Melaka, Malaysia
Duration: 27 Nov 201729 Nov 2017

Publication series

NameProceeding of 2017 International Conference on Robotics, Automation and Sciences, ICORAS 2017
Volume2018-March

Conference

Conference2017 International Conference on Robotics, Automation and Sciences, ICORAS 2017
Country/TerritoryMalaysia
CityMelaka
Period27/11/1729/11/17

Keywords

  • Binarized Statistical Image Features
  • Gait
  • Gait Recognition
  • Histograms of Oriented Gradients
  • Motion History Image

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Information Systems and Management
  • Control and Optimization
  • Health Informatics

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