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

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

doi:10.1007/978-3-319-50835-1_71

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings
Editors	George Bebis, Bahram Parvin, Sandra Skaff, Daisuke Iwai, Richard Boyle, Darko Koracin, Fatih Porikli, Carlos Scheidegger, Alireza Entezari, Jianyuan Min, Amela Sadagic, Tobias Isenberg
Publisher	Springer Verlag
Pages	790-799
Number of pages	10
ISBN (Print)	9783319508344
DOIs	https://doi.org/10.1007/978-3-319-50835-1_71
State	Published - 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10072 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

Access to Document

10.1007/978-3-319-50835-1_71

Cite this

Binsaadoon, A. G., & El-Alfy, E. S. M. (2016). Multi-kernel fuzzy-based local gabor patterns for gait recognition. In G. Bebis, B. Parvin, S. Skaff, D. Iwai, R. Boyle, D. Koracin, F. Porikli, C. Scheidegger, A. Entezari, J. Min, A. Sadagic, & T. Isenberg (Eds.), Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings (pp. 790-799). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10072 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-50835-1_71

Binsaadoon, Amer G. ; El-Alfy, El Sayed M. / Multi-kernel fuzzy-based local gabor patterns for gait recognition. Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings. editor / George Bebis ; Bahram Parvin ; Sandra Skaff ; Daisuke Iwai ; Richard Boyle ; Darko Koracin ; Fatih Porikli ; Carlos Scheidegger ; Alireza Entezari ; Jianyuan Min ; Amela Sadagic ; Tobias Isenberg. Springer Verlag, 2016. pp. 790-799 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Multi-kernel fuzzy-based local gabor patterns for gait recognition",

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.",

author = "Binsaadoon, \{Amer G.\} and El-Alfy, \{El Sayed M.\}",

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Binsaadoon, AG & El-Alfy, ESM 2016, Multi-kernel fuzzy-based local gabor patterns for gait recognition. in G Bebis, B Parvin, S Skaff, D Iwai, R Boyle, D Koracin, F Porikli, C Scheidegger, A Entezari, J Min, A Sadagic & T Isenberg (eds), Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10072 LNCS, Springer Verlag, pp. 790-799. https://doi.org/10.1007/978-3-319-50835-1_71

Multi-kernel fuzzy-based local gabor patterns for gait recognition. / Binsaadoon, Amer G.; El-Alfy, El Sayed M.
Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings. ed. / George Bebis; Bahram Parvin; Sandra Skaff; Daisuke Iwai; Richard Boyle; Darko Koracin; Fatih Porikli; Carlos Scheidegger; Alireza Entezari; Jianyuan Min; Amela Sadagic; Tobias Isenberg. Springer Verlag, 2016. p. 790-799 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10072 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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A2 - Iwai, Daisuke

A2 - Boyle, Richard

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A2 - Porikli, Fatih

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Binsaadoon AG, El-Alfy ESM. Multi-kernel fuzzy-based local gabor patterns for gait recognition. In Bebis G, Parvin B, Skaff S, Iwai D, Boyle R, Koracin D, Porikli F, Scheidegger C, Entezari A, Min J, Sadagic A, Isenberg T, editors, Advances in Visual Computing - 12th International Symposium, ISVC 2016, Proceedings. Springer Verlag. 2016. p. 790-799. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-50835-1_71

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

Abstract

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