Multi-view Multi-modal Gait Based Human Identity Recognition from Surveillance videos

Emdad Hossain; Girija CHETTY; Roland GOECKE

doi:10.1007/978-3-642-37081-6_11

Multi-view Multi-modal Gait Based Human Identity Recognition from Surveillance videos

Emdad Hossain, Girija CHETTY, Roland GOECKE

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

In this paper we propose a novel human-identification scheme from long range gait profiles in surveillance videos. We investigate the role of multi view gait images acquired from multiple cameras, the importance of infrared and visible range images in ascertaining identity, the impact of multimodal fusion, efficient subspace features and classifier methods, and the role of soft/secondary biometric (walking style) in enhancing the accuracy and robustness of the identification systems, Experimental evaluation of several subspace based gait feature extraction approaches (PCA/LDA) and learning classifier methods (NB/MLP/SVM/SMO) on different datasets from a publicly available gait database CASIA, show significant improvement in recognition accuracies with multimodal fusion of multi-view gait images from visible and infrared cameras acquired from video surveillance scenarios.

Original language	English
Title of host publication	Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
Editors	Friedhelm Schwenker, Stefan Scherer, Louis-Philippe Morency
Place of Publication	Berling Heidelberg
Publisher	Springer
Pages	88-99
Number of pages	12
Volume	7742
ISBN (Electronic)	9783642370816
ISBN (Print)	9783642370809
DOIs	https://doi.org/10.1007/978-3-642-37081-6_11
Publication status	Published - 2013
Event	IAPR TC3 Workshop, MPRSS 2012, Revised Selected Papers: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction - Tsukuba, Tsukuba, Japan Duration: 11 Nov 2012 → 15 Nov 2012 http://www.icpr2012.org/ (Conference webpage)

Conference

Conference	IAPR TC3 Workshop, MPRSS 2012, Revised Selected Papers
Country/Territory	Japan
City	Tsukuba
Period	11/11/12 → 15/11/12
Internet address	http://www.icpr2012.org/ (Conference webpage)

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10.1007/978-3-642-37081-6_11

Cite this

@inproceedings{0855e8a85e814a31af2eb508c829b9d7,

title = "Multi-view Multi-modal Gait Based Human Identity Recognition from Surveillance videos",

abstract = "In this paper we propose a novel human-identification scheme from long range gait profiles in surveillance videos. We investigate the role of multi view gait images acquired from multiple cameras, the importance of infrared and visible range images in ascertaining identity, the impact of multimodal fusion, efficient subspace features and classifier methods, and the role of soft/secondary biometric (walking style) in enhancing the accuracy and robustness of the identification systems, Experimental evaluation of several subspace based gait feature extraction approaches (PCA/LDA) and learning classifier methods (NB/MLP/SVM/SMO) on different datasets from a publicly available gait database CASIA, show significant improvement in recognition accuracies with multimodal fusion of multi-view gait images from visible and infrared cameras acquired from video surveillance scenarios.",

keywords = "Multiview, Identification, Fusion",

author = "Emdad Hossain and Girija CHETTY and Roland GOECKE",

year = "2013",

doi = "10.1007/978-3-642-37081-6_11",

language = "English",

isbn = "9783642370809",

volume = "7742",

pages = "88--99",

editor = "Friedhelm Schwenker and Stefan Scherer and Louis-Philippe Morency",

booktitle = "Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction",

publisher = "Springer",

address = "Netherlands",

note = "IAPR TC3 Workshop, MPRSS 2012, Revised Selected Papers : Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction ; Conference date: 11-11-2012 Through 15-11-2012",

url = "http://www.icpr2012.org/",

}

Hossain, E, CHETTY, G & GOECKE, R 2013, Multi-view Multi-modal Gait Based Human Identity Recognition from Surveillance videos. in F Schwenker, S Scherer & L-P Morency (eds), Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. vol. 7742, Springer, Berling Heidelberg, pp. 88-99, IAPR TC3 Workshop, MPRSS 2012, Revised Selected Papers, Tsukuba, Japan, 11/11/12. https://doi.org/10.1007/978-3-642-37081-6_11

Multi-view Multi-modal Gait Based Human Identity Recognition from Surveillance videos. / Hossain, Emdad; CHETTY, Girija; GOECKE, Roland.
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. ed. / Friedhelm Schwenker; Stefan Scherer; Louis-Philippe Morency. Vol. 7742 Berling Heidelberg: Springer, 2013. p. 88-99.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Multi-view Multi-modal Gait Based Human Identity Recognition from Surveillance videos

AU - Hossain, Emdad

AU - CHETTY, Girija

AU - GOECKE, Roland

PY - 2013

Y1 - 2013

N2 - In this paper we propose a novel human-identification scheme from long range gait profiles in surveillance videos. We investigate the role of multi view gait images acquired from multiple cameras, the importance of infrared and visible range images in ascertaining identity, the impact of multimodal fusion, efficient subspace features and classifier methods, and the role of soft/secondary biometric (walking style) in enhancing the accuracy and robustness of the identification systems, Experimental evaluation of several subspace based gait feature extraction approaches (PCA/LDA) and learning classifier methods (NB/MLP/SVM/SMO) on different datasets from a publicly available gait database CASIA, show significant improvement in recognition accuracies with multimodal fusion of multi-view gait images from visible and infrared cameras acquired from video surveillance scenarios.

AB - In this paper we propose a novel human-identification scheme from long range gait profiles in surveillance videos. We investigate the role of multi view gait images acquired from multiple cameras, the importance of infrared and visible range images in ascertaining identity, the impact of multimodal fusion, efficient subspace features and classifier methods, and the role of soft/secondary biometric (walking style) in enhancing the accuracy and robustness of the identification systems, Experimental evaluation of several subspace based gait feature extraction approaches (PCA/LDA) and learning classifier methods (NB/MLP/SVM/SMO) on different datasets from a publicly available gait database CASIA, show significant improvement in recognition accuracies with multimodal fusion of multi-view gait images from visible and infrared cameras acquired from video surveillance scenarios.

KW - Multiview

KW - Identification

KW - Fusion

U2 - 10.1007/978-3-642-37081-6_11

DO - 10.1007/978-3-642-37081-6_11

M3 - Conference contribution

SN - 9783642370809

VL - 7742

SP - 88

EP - 99

BT - Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

A2 - Schwenker, Friedhelm

A2 - Scherer, Stefan

A2 - Morency, Louis-Philippe

PB - Springer

CY - Berling Heidelberg

T2 - IAPR TC3 Workshop, MPRSS 2012, Revised Selected Papers

Y2 - 11 November 2012 through 15 November 2012

ER -

Multi-view Multi-modal Gait Based Human Identity Recognition from Surveillance videos

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