Multi-kernel fuzzy-based local gabor patterns for gait recognition

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

*Corresponding author for this work

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

1 Scopus citations

Abstract

This paper proposes a novel multi-kernel fuzzy-based local Gabor binary patterns method (MFLGBP) for the purpose of gait representation and recognition. First, we construct the gait energy image (GEI) from mean motion cycle of a gait sequence. Then, we apply Gabor filters and encode the variations in the Gabor magnitude by using a kernel-based fuzzy local binary pattern (KFLBP) operator. Finally, classification is performed using a support vector machine (SVM). Experiments are carried out using the benchmark CASIA B gait database. Our proposed feature extraction method has shown promising performance in terms of correct recognition rate as compared to other methods.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings
EditorsGeorge Bebis, Bahram Parvin, Sandra Skaff, Daisuke Iwai, Richard Boyle, Darko Koracin, Fatih Porikli, Carlos Scheidegger, Alireza Entezari, Jianyuan Min, Amela Sadagic, Tobias Isenberg
PublisherSpringer Verlag
Pages790-799
Number of pages10
ISBN (Print)9783319508344
DOIs
StatePublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10072 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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