Spatio-temporal texture-based feature extraction for spontaneous facial expression recognition

Siti Khairuni Amalina Kamarol, Nor Syazana Meli, Mohamed Hisham Jaward, Nader Kamrani

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    1 Citation (Scopus)

    Abstract

    Recently, recognition of naturalistic expressions known as spontaneous facial expressions has attracted attention from researchers due to its significant application in behavioral and clinical research. Currently, most of the work consider recognition of posed expressions. In this paper, we propose a spatio-temporal feature extraction method, Spatio-Temporal Texture Map (STTM), for recognition of spontaneous expressions and compare its performance against that of state-of-the-art feature extraction methods. Both appearance-based and geometry-based feature extraction approaches are considered for comparisons against STTM. The appearance-based techniques considered are Volume Local Binary Pattern (VLBP) and Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) whereas a multi-view tree-based face detector is considered as a geometry-based technique. Support Vector Machine (SVM) is used as the classifier where the extracted features are classified into classes of naturalistic expressions. The feature extraction methods are evaluated over the spontaneous facial expression data from CASME II database. Experimental results show that STTM is capable of recognizing spontaneous expressions and outperforming the other methods in terms of recognition rate, accuracy and computational cost.
    Original languageEnglish
    Title of host publicationProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
    EditorsNorimichi Ukita, Eigo Segawa, Norichika Yui
    Place of PublicationNew Jersey USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages467 - 470
    Number of pages4
    ISBN (Print)9784901122146
    DOIs
    Publication statusPublished - 2015
    EventMachine Vision Applications 2015 - Tokyo Japan, Tokyo, Japan
    Duration: 18 May 201522 May 2015
    Conference number: 14

    Conference

    ConferenceMachine Vision Applications 2015
    Abbreviated titleMVA
    CountryJapan
    CityTokyo
    Period18/05/1522/05/15
    Other14th IAPR International Conference on Machine Vision Applications, MVA 2015

    Cite this

    Kamarol, S. K. A., Meli, N. S., Jaward, M. H., & Kamrani, N. (2015). Spatio-temporal texture-based feature extraction for spontaneous facial expression recognition. In N. Ukita, E. Segawa, & N. Yui (Eds.), Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015 (pp. 467 - 470). New Jersey USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/MVA.2015.7153112
    Kamarol, Siti Khairuni Amalina ; Meli, Nor Syazana ; Jaward, Mohamed Hisham ; Kamrani, Nader. / Spatio-temporal texture-based feature extraction for spontaneous facial expression recognition. Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015. editor / Norimichi Ukita ; Eigo Segawa ; Norichika Yui. New Jersey USA : IEEE, Institute of Electrical and Electronics Engineers, 2015. pp. 467 - 470
    @inproceedings{64223d11e4814e1890c7ee80eccd71eb,
    title = "Spatio-temporal texture-based feature extraction for spontaneous facial expression recognition",
    abstract = "Recently, recognition of naturalistic expressions known as spontaneous facial expressions has attracted attention from researchers due to its significant application in behavioral and clinical research. Currently, most of the work consider recognition of posed expressions. In this paper, we propose a spatio-temporal feature extraction method, Spatio-Temporal Texture Map (STTM), for recognition of spontaneous expressions and compare its performance against that of state-of-the-art feature extraction methods. Both appearance-based and geometry-based feature extraction approaches are considered for comparisons against STTM. The appearance-based techniques considered are Volume Local Binary Pattern (VLBP) and Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) whereas a multi-view tree-based face detector is considered as a geometry-based technique. Support Vector Machine (SVM) is used as the classifier where the extracted features are classified into classes of naturalistic expressions. The feature extraction methods are evaluated over the spontaneous facial expression data from CASME II database. Experimental results show that STTM is capable of recognizing spontaneous expressions and outperforming the other methods in terms of recognition rate, accuracy and computational cost.",
    author = "Kamarol, {Siti Khairuni Amalina} and Meli, {Nor Syazana} and Jaward, {Mohamed Hisham} and Nader Kamrani",
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    doi = "10.1109/MVA.2015.7153112",
    language = "English",
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    Kamarol, SKA, Meli, NS, Jaward, MH & Kamrani, N 2015, Spatio-temporal texture-based feature extraction for spontaneous facial expression recognition. in N Ukita, E Segawa & N Yui (eds), Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015. IEEE, Institute of Electrical and Electronics Engineers, New Jersey USA, pp. 467 - 470, Machine Vision Applications 2015, Tokyo, Japan, 18/05/15. https://doi.org/10.1109/MVA.2015.7153112

    Spatio-temporal texture-based feature extraction for spontaneous facial expression recognition. / Kamarol, Siti Khairuni Amalina; Meli, Nor Syazana; Jaward, Mohamed Hisham; Kamrani, Nader.

    Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015. ed. / Norimichi Ukita; Eigo Segawa; Norichika Yui. New Jersey USA : IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 467 - 470.

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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    T1 - Spatio-temporal texture-based feature extraction for spontaneous facial expression recognition

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    AU - Meli, Nor Syazana

    AU - Jaward, Mohamed Hisham

    AU - Kamrani, Nader

    PY - 2015

    Y1 - 2015

    N2 - Recently, recognition of naturalistic expressions known as spontaneous facial expressions has attracted attention from researchers due to its significant application in behavioral and clinical research. Currently, most of the work consider recognition of posed expressions. In this paper, we propose a spatio-temporal feature extraction method, Spatio-Temporal Texture Map (STTM), for recognition of spontaneous expressions and compare its performance against that of state-of-the-art feature extraction methods. Both appearance-based and geometry-based feature extraction approaches are considered for comparisons against STTM. The appearance-based techniques considered are Volume Local Binary Pattern (VLBP) and Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) whereas a multi-view tree-based face detector is considered as a geometry-based technique. Support Vector Machine (SVM) is used as the classifier where the extracted features are classified into classes of naturalistic expressions. The feature extraction methods are evaluated over the spontaneous facial expression data from CASME II database. Experimental results show that STTM is capable of recognizing spontaneous expressions and outperforming the other methods in terms of recognition rate, accuracy and computational cost.

    AB - Recently, recognition of naturalistic expressions known as spontaneous facial expressions has attracted attention from researchers due to its significant application in behavioral and clinical research. Currently, most of the work consider recognition of posed expressions. In this paper, we propose a spatio-temporal feature extraction method, Spatio-Temporal Texture Map (STTM), for recognition of spontaneous expressions and compare its performance against that of state-of-the-art feature extraction methods. Both appearance-based and geometry-based feature extraction approaches are considered for comparisons against STTM. The appearance-based techniques considered are Volume Local Binary Pattern (VLBP) and Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) whereas a multi-view tree-based face detector is considered as a geometry-based technique. Support Vector Machine (SVM) is used as the classifier where the extracted features are classified into classes of naturalistic expressions. The feature extraction methods are evaluated over the spontaneous facial expression data from CASME II database. Experimental results show that STTM is capable of recognizing spontaneous expressions and outperforming the other methods in terms of recognition rate, accuracy and computational cost.

    U2 - 10.1109/MVA.2015.7153112

    DO - 10.1109/MVA.2015.7153112

    M3 - Conference Paper

    SN - 9784901122146

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    BT - Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015

    A2 - Ukita, Norimichi

    A2 - Segawa, Eigo

    A2 - Yui, Norichika

    PB - IEEE, Institute of Electrical and Electronics Engineers

    CY - New Jersey USA

    ER -

    Kamarol SKA, Meli NS, Jaward MH, Kamrani N. Spatio-temporal texture-based feature extraction for spontaneous facial expression recognition. In Ukita N, Segawa E, Yui N, editors, Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015. New Jersey USA: IEEE, Institute of Electrical and Electronics Engineers. 2015. p. 467 - 470 https://doi.org/10.1109/MVA.2015.7153112