Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields

Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic

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

    25 Citations (Scopus)
    10 Downloads (Pure)

    Abstract

    Automatic pain intensity estimation from facial images is challenging mainly because of high variability in subject-specific pain expressiveness. This heterogeneity in the subjects causes their facial appearance to vary significantly when experiencing the same pain level. The standard classification methods (e.g., SVMs) do not provide a principled way of accounting for this heterogeneity. To this end, we propose the heteroscedastic Conditional Ordinal Random Field (CORF) model for automatic estimation of pain intensity. This model generalizes the CORF framework for modeling sequences of ordinal variables, by adapting it for heteroscedasticity. This is attained by allowing the variance in the ordinal probit model in the CORF to change depending on the input features, resulting in the model able to adapt to the pain expressiveness level specific to each subject. Our experimental results on the UNBC Shoulder Pain Database show that modeling heterogeneity in the subjects with the framework of CORFs improves the pain intensity estimation attained by the standard CORF model, and the other commonly used classification models.
    Original languageEnglish
    Title of host publicationAdvances in Visual Computing
    Subtitle of host publication9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings
    EditorsSabine Coquillart, Xun Luo, Min Chen, David Gotz
    Place of PublicationBerlin
    PublisherSpringer
    Pages234-243
    Number of pages10
    ISBN (Print)978-3-642-41938-6
    DOIs
    Publication statusPublished - Jul 2013
    Event9th International Symposium on Visual Computing, ISVC 2013 - Rethymnon, Crete, Greece
    Duration: 29 Jul 201331 Jul 2013
    Conference number: 9

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Volume8034
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference9th International Symposium on Visual Computing, ISVC 2013
    Abbreviated titleISVC
    CountryGreece
    CityRethymnon, Crete
    Period29/07/1331/07/13

    Keywords

    • EWI-24340
    • METIS-302660
    • IR-89371
    • HMI-HF: Human Factors

    Cite this

    Rudovic, O., Pavlovic, V., & Pantic, M. (2013). Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields. In S. Coquillart, X. Luo, M. Chen, & D. Gotz (Eds.), Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings (pp. 234-243). (Lecture Notes in Computer Science; Vol. 8034). Berlin: Springer. https://doi.org/10.1007/978-3-642-41939-3_23
    Rudovic, Ognjen ; Pavlovic, Vladimir ; Pantic, Maja. / Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields. Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings. editor / Sabine Coquillart ; Xun Luo ; Min Chen ; David Gotz. Berlin : Springer, 2013. pp. 234-243 (Lecture Notes in Computer Science).
    @inproceedings{29a2adec3e1e4b29ba1ecace72c6b90d,
    title = "Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields",
    abstract = "Automatic pain intensity estimation from facial images is challenging mainly because of high variability in subject-specific pain expressiveness. This heterogeneity in the subjects causes their facial appearance to vary significantly when experiencing the same pain level. The standard classification methods (e.g., SVMs) do not provide a principled way of accounting for this heterogeneity. To this end, we propose the heteroscedastic Conditional Ordinal Random Field (CORF) model for automatic estimation of pain intensity. This model generalizes the CORF framework for modeling sequences of ordinal variables, by adapting it for heteroscedasticity. This is attained by allowing the variance in the ordinal probit model in the CORF to change depending on the input features, resulting in the model able to adapt to the pain expressiveness level specific to each subject. Our experimental results on the UNBC Shoulder Pain Database show that modeling heterogeneity in the subjects with the framework of CORFs improves the pain intensity estimation attained by the standard CORF model, and the other commonly used classification models.",
    keywords = "EWI-24340, METIS-302660, IR-89371, HMI-HF: Human Factors",
    author = "Ognjen Rudovic and Vladimir Pavlovic and Maja Pantic",
    year = "2013",
    month = "7",
    doi = "10.1007/978-3-642-41939-3_23",
    language = "English",
    isbn = "978-3-642-41938-6",
    series = "Lecture Notes in Computer Science",
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    Rudovic, O, Pavlovic, V & Pantic, M 2013, Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields. in S Coquillart, X Luo, M Chen & D Gotz (eds), Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings. Lecture Notes in Computer Science, vol. 8034, Springer, Berlin, pp. 234-243, 9th International Symposium on Visual Computing, ISVC 2013, Rethymnon, Crete, Greece, 29/07/13. https://doi.org/10.1007/978-3-642-41939-3_23

    Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields. / Rudovic, Ognjen; Pavlovic, Vladimir; Pantic, Maja.

    Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings. ed. / Sabine Coquillart; Xun Luo; Min Chen; David Gotz. Berlin : Springer, 2013. p. 234-243 (Lecture Notes in Computer Science; Vol. 8034).

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

    TY - GEN

    T1 - Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields

    AU - Rudovic, Ognjen

    AU - Pavlovic, Vladimir

    AU - Pantic, Maja

    PY - 2013/7

    Y1 - 2013/7

    N2 - Automatic pain intensity estimation from facial images is challenging mainly because of high variability in subject-specific pain expressiveness. This heterogeneity in the subjects causes their facial appearance to vary significantly when experiencing the same pain level. The standard classification methods (e.g., SVMs) do not provide a principled way of accounting for this heterogeneity. To this end, we propose the heteroscedastic Conditional Ordinal Random Field (CORF) model for automatic estimation of pain intensity. This model generalizes the CORF framework for modeling sequences of ordinal variables, by adapting it for heteroscedasticity. This is attained by allowing the variance in the ordinal probit model in the CORF to change depending on the input features, resulting in the model able to adapt to the pain expressiveness level specific to each subject. Our experimental results on the UNBC Shoulder Pain Database show that modeling heterogeneity in the subjects with the framework of CORFs improves the pain intensity estimation attained by the standard CORF model, and the other commonly used classification models.

    AB - Automatic pain intensity estimation from facial images is challenging mainly because of high variability in subject-specific pain expressiveness. This heterogeneity in the subjects causes their facial appearance to vary significantly when experiencing the same pain level. The standard classification methods (e.g., SVMs) do not provide a principled way of accounting for this heterogeneity. To this end, we propose the heteroscedastic Conditional Ordinal Random Field (CORF) model for automatic estimation of pain intensity. This model generalizes the CORF framework for modeling sequences of ordinal variables, by adapting it for heteroscedasticity. This is attained by allowing the variance in the ordinal probit model in the CORF to change depending on the input features, resulting in the model able to adapt to the pain expressiveness level specific to each subject. Our experimental results on the UNBC Shoulder Pain Database show that modeling heterogeneity in the subjects with the framework of CORFs improves the pain intensity estimation attained by the standard CORF model, and the other commonly used classification models.

    KW - EWI-24340

    KW - METIS-302660

    KW - IR-89371

    KW - HMI-HF: Human Factors

    U2 - 10.1007/978-3-642-41939-3_23

    DO - 10.1007/978-3-642-41939-3_23

    M3 - Conference contribution

    SN - 978-3-642-41938-6

    T3 - Lecture Notes in Computer Science

    SP - 234

    EP - 243

    BT - Advances in Visual Computing

    A2 - Coquillart, Sabine

    A2 - Luo, Xun

    A2 - Chen, Min

    A2 - Gotz, David

    PB - Springer

    CY - Berlin

    ER -

    Rudovic O, Pavlovic V, Pantic M. Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields. In Coquillart S, Luo X, Chen M, Gotz D, editors, Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings. Berlin: Springer. 2013. p. 234-243. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-41939-3_23