Kernel-Based Fuzzy Local Binary Pattern for Gait Recognition

Amer G. Binsaadoon, El Sayed M. El-Alfy

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

2 Scopus citations

Abstract

Gait recognition has received increasing attention in biometrics. However, more effort is needed to enhance the performance. In this paper, we investigate a novel descriptor for gait recognition known as Kernel-based Fuzzy Local Binary Pattern (KFLBP). The spatio-Temporal static and dynamic characteristics of a gait sequence is first summarized using a Gait-Energy Image (GEI). Then, the proposed approach combines multiple FLBP with different radii to better handle uncertainty in GEI and improve the recognition performance. We evaluate the proposed method on CASIA B dataset at different view angles. We also compare the performance with other feature extraction methods and explore the impact of different walking covariates on the performance.

Original languageEnglish
Title of host publicationProceedings - UKSim-AMSS 2016
Subtitle of host publication10th European Modelling Symposium on Computer Modelling and Simulation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages35-40
Number of pages6
ISBN (Electronic)9781509049707
DOIs
StatePublished - 4 May 2017

Publication series

NameProceedings - UKSim-AMSS 2016: 10th European Modelling Symposium on Computer Modelling and Simulation

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Gait recognition; human motion; image processing; multi-kernel; fuzzy logic; local binary patterns; gait energy image.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence
  • Modeling and Simulation

Fingerprint

Dive into the research topics of 'Kernel-Based Fuzzy Local Binary Pattern for Gait Recognition'. Together they form a unique fingerprint.

Cite this