Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition

Emdad Hossain, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contribution

10 Citations (Scopus)

Abstract

In this paper we propose a novel multimodal feature learning technique based on deep learning for gait biometric based human-identification scheme from surveillance videos. Experimental evaluation of proposed learning features based on novel deep learning and standard (PCA/LDA) features in combination with classifier techniques (NN/MLP/SVM/SMO) on different datasets from two gait databases (the publicly available CASIA multiview multispectral database, and the UCMG multiview database), show a significant improvement in recognition accuracies with proposed fused deep learning features.
Original languageEnglish
Title of host publication20th International Conference on Neural Information Processing (ICONIP 2013)
Subtitle of host publicationLecture Notes in Computer Science
EditorsMinho Lee, Akira Hirose, Zeng-Guang, Hou, Rhee Man Kil
Place of PublicationGermany
PublisherSpringer
Pages721-728
Number of pages8
Volume8227
ISBN (Electronic)9783642420429
ISBN (Print)9783642420412
DOIs
Publication statusPublished - 2013
Event20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013

Conference

Conference20th International Conference on Neural Information Processing (ICONIP 2013)
Abbreviated titleICONIP 2013
CountryKorea, Republic of
CityDaegu
Period3/11/137/11/13

Fingerprint

Biometrics
Classifiers
Deep learning

Cite this

Hossain, E., & CHETTY, G. (2013). Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition. In M. Lee, A. Hirose, Zeng-Guang, Hou, & R. M. Kil (Eds.), 20th International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science (Vol. 8227, pp. 721-728). Germany: Springer. https://doi.org/10.1007/978-3-642-42042-9_89
Hossain, Emdad ; CHETTY, Girija. / Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition. 20th International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. editor / Minho Lee ; Akira Hirose ; Zeng-Guang ; Hou ; Rhee Man Kil. Vol. 8227 Germany : Springer, 2013. pp. 721-728
@inproceedings{08b4c0d74c204a41b7b3bd899dee0fed,
title = "Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition",
abstract = "In this paper we propose a novel multimodal feature learning technique based on deep learning for gait biometric based human-identification scheme from surveillance videos. Experimental evaluation of proposed learning features based on novel deep learning and standard (PCA/LDA) features in combination with classifier techniques (NN/MLP/SVM/SMO) on different datasets from two gait databases (the publicly available CASIA multiview multispectral database, and the UCMG multiview database), show a significant improvement in recognition accuracies with proposed fused deep learning features.",
keywords = "Multimodal, Deep learning, Fusion, Multiview, Gait, vPCA, Identification",
author = "Emdad Hossain and Girija CHETTY",
year = "2013",
doi = "10.1007/978-3-642-42042-9_89",
language = "English",
isbn = "9783642420412",
volume = "8227",
pages = "721--728",
editor = "Minho Lee and Akira Hirose and Zeng-Guang and Hou and Kil, {Rhee Man}",
booktitle = "20th International Conference on Neural Information Processing (ICONIP 2013)",
publisher = "Springer",
address = "Netherlands",

}

Hossain, E & CHETTY, G 2013, Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition. in M Lee, A Hirose, Zeng-Guang, Hou & RM Kil (eds), 20th International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. vol. 8227, Springer, Germany, pp. 721-728, 20th International Conference on Neural Information Processing (ICONIP 2013), Daegu, Korea, Republic of, 3/11/13. https://doi.org/10.1007/978-3-642-42042-9_89

Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition. / Hossain, Emdad; CHETTY, Girija.

20th International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. ed. / Minho Lee; Akira Hirose; Zeng-Guang; Hou; Rhee Man Kil. Vol. 8227 Germany : Springer, 2013. p. 721-728.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition

AU - Hossain, Emdad

AU - CHETTY, Girija

PY - 2013

Y1 - 2013

N2 - In this paper we propose a novel multimodal feature learning technique based on deep learning for gait biometric based human-identification scheme from surveillance videos. Experimental evaluation of proposed learning features based on novel deep learning and standard (PCA/LDA) features in combination with classifier techniques (NN/MLP/SVM/SMO) on different datasets from two gait databases (the publicly available CASIA multiview multispectral database, and the UCMG multiview database), show a significant improvement in recognition accuracies with proposed fused deep learning features.

AB - In this paper we propose a novel multimodal feature learning technique based on deep learning for gait biometric based human-identification scheme from surveillance videos. Experimental evaluation of proposed learning features based on novel deep learning and standard (PCA/LDA) features in combination with classifier techniques (NN/MLP/SVM/SMO) on different datasets from two gait databases (the publicly available CASIA multiview multispectral database, and the UCMG multiview database), show a significant improvement in recognition accuracies with proposed fused deep learning features.

KW - Multimodal

KW - Deep learning

KW - Fusion

KW - Multiview

KW - Gait

KW - vPCA

KW - Identification

UR - https://link.springer.com/chapter/10.1007%2F978-3-642-42042-9_89

U2 - 10.1007/978-3-642-42042-9_89

DO - 10.1007/978-3-642-42042-9_89

M3 - Conference contribution

SN - 9783642420412

VL - 8227

SP - 721

EP - 728

BT - 20th International Conference on Neural Information Processing (ICONIP 2013)

A2 - Lee, Minho

A2 - Hirose, Akira

A2 - Zeng-Guang, null

A2 - Hou, null

A2 - Kil, Rhee Man

PB - Springer

CY - Germany

ER -

Hossain E, CHETTY G. Multimodal Feature Learning for Gait Biometric Base Human Identity Recognition. In Lee M, Hirose A, Zeng-Guang, Hou, Kil RM, editors, 20th International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. Vol. 8227. Germany: Springer. 2013. p. 721-728 https://doi.org/10.1007/978-3-642-42042-9_89