Multimodal identity verification based on learning face and gait cues

Emdad Hossain, Girija Chetty

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

18 Citations (Scopus)

Abstract

In this paper we propose a novel multimodal Bayesian approach based on PCA-LDA processing for person identification from low resolution surveillance video with cues extracted from gait and face biometrics. The experimental evaluation of the proposed scheme on a publicly available database [2] showed that the combined PCA-LDA face and gait features can lead to powerful identity verification and can capture the inherent multimodality in walking gait patterns and discriminate the identity from low resolution surveillance videos.

Original languageEnglish
Title of host publicationNeural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Subtitle of host publicationLecture Notes in Computer Science
EditorsBL Lu, L Zhang, J Kwok
PublisherSpringer
Pages1-8
Number of pages8
Volume7064
EditionPART 3
ISBN (Electronic)9783642249655
ISBN (Print)9783642249648
DOIs
Publication statusPublished - 2011
EventInternational Conference on Neural Information Processing ICONIP 2011 - Shanghai, Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7064 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Neural Information Processing ICONIP 2011
Abbreviated titleICONIP 2011
CountryChina
CityShanghai
Period13/11/1117/11/11

Fingerprint

Gait
Biometrics
Video Surveillance
Face
Processing
Multimodality
Experimental Evaluation
Bayesian Approach
Person
Learning

Cite this

Hossain, E., & Chetty, G. (2011). Multimodal identity verification based on learning face and gait cues. In BL. Lu, L. Zhang, & J. Kwok (Eds.), Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings: Lecture Notes in Computer Science (PART 3 ed., Vol. 7064, pp. 1-8). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064 LNCS, No. PART 3). Springer. https://doi.org/10.1007/978-3-642-24965-5_1
Hossain, Emdad ; Chetty, Girija. / Multimodal identity verification based on learning face and gait cues. Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings: Lecture Notes in Computer Science. editor / BL Lu ; L Zhang ; J Kwok. Vol. 7064 PART 3. ed. Springer, 2011. pp. 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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title = "Multimodal identity verification based on learning face and gait cues",
abstract = "In this paper we propose a novel multimodal Bayesian approach based on PCA-LDA processing for person identification from low resolution surveillance video with cues extracted from gait and face biometrics. The experimental evaluation of the proposed scheme on a publicly available database [2] showed that the combined PCA-LDA face and gait features can lead to powerful identity verification and can capture the inherent multimodality in walking gait patterns and discriminate the identity from low resolution surveillance videos.",
keywords = "Bayesian, Biometric, gait recognition, k-NN, LDA, PCA",
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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
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Hossain, E & Chetty, G 2011, Multimodal identity verification based on learning face and gait cues. in BL Lu, L Zhang & J Kwok (eds), Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings: Lecture Notes in Computer Science. PART 3 edn, vol. 7064, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 7064 LNCS, Springer, pp. 1-8, International Conference on Neural Information Processing ICONIP 2011, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24965-5_1

Multimodal identity verification based on learning face and gait cues. / Hossain, Emdad; Chetty, Girija.

Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings: Lecture Notes in Computer Science. ed. / BL Lu; L Zhang; J Kwok. Vol. 7064 PART 3. ed. Springer, 2011. p. 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064 LNCS, No. PART 3).

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

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AU - Chetty, Girija

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AB - In this paper we propose a novel multimodal Bayesian approach based on PCA-LDA processing for person identification from low resolution surveillance video with cues extracted from gait and face biometrics. The experimental evaluation of the proposed scheme on a publicly available database [2] showed that the combined PCA-LDA face and gait features can lead to powerful identity verification and can capture the inherent multimodality in walking gait patterns and discriminate the identity from low resolution surveillance videos.

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Hossain E, Chetty G. Multimodal identity verification based on learning face and gait cues. In Lu BL, Zhang L, Kwok J, editors, Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings: Lecture Notes in Computer Science. PART 3 ed. Vol. 7064. Springer. 2011. p. 1-8. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-24965-5_1