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

    17 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).
    @inproceedings{54cdea6214644d3b90f90f7fd312efdb,
    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",
    author = "Emdad Hossain and Girija Chetty",
    year = "2011",
    doi = "10.1007/978-3-642-24965-5_1",
    language = "English",
    isbn = "9783642249648",
    volume = "7064",
    series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    publisher = "Springer",
    number = "PART 3",
    pages = "1--8",
    editor = "BL Lu and L Zhang and J Kwok",
    booktitle = "Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings",
    address = "Netherlands",
    edition = "PART 3",

    }

    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

    TY - GEN

    T1 - Multimodal identity verification based on learning face and gait cues

    AU - Hossain, Emdad

    AU - Chetty, Girija

    PY - 2011

    Y1 - 2011

    N2 - 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.

    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.

    KW - Bayesian

    KW - Biometric

    KW - gait recognition

    KW - k-NN

    KW - LDA

    KW - PCA

    UR - http://www.scopus.com/inward/record.url?scp=81855218221&partnerID=8YFLogxK

    U2 - 10.1007/978-3-642-24965-5_1

    DO - 10.1007/978-3-642-24965-5_1

    M3 - Conference contribution

    SN - 9783642249648

    VL - 7064

    T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    SP - 1

    EP - 8

    BT - Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings

    A2 - Lu, BL

    A2 - Zhang, L

    A2 - Kwok, J

    PB - Springer

    ER -

    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