On pain assessment from facial videos using spatio-temporal local descriptors

Ruijing Yang, Shujun Tong, Miguel Bordallo, Elhocine Boutellaa, Jinye Peng, Xiaoyi Feng, Abdenour Hadid

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

2 Citations (Scopus)

Abstract

Automatically recognizing pain from spontaneous facial expression is of increased attention, since it can provide for a direct and relatively objective indication to pain experience. Until now, most of the existing works have focused on analyzing pain from individual images or video-frames, hence discarding the spatio-temporal information that can be useful in the continuous assessment of pain. In this context, this paper investigates and quantifies for the first time the role of the spatio-temporal information in pain assessment by comparing the performance of several baseline local descriptors used in their traditional spatial form against their spatio-temporal counterparts that take into account the video dynamics. For this purpose, we perform extensive experiments on two benchmark datasets. Our results indicate that using spatio-temporal information to classify video-sequences consistently shows superior performance when compared against the one obtained using only static information.
Original languageEnglish
Title of host publication2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
PublisherInstitute of Electrical and Electronic Engineers IEEE
Number of pages6
ISBN (Electronic)978-1-4673-8910-5
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication

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Experiments

Keywords

  • automatic pain assessment
  • facial expression
  • spatio-temporal features
  • LBP

Cite this

Yang, R., Tong, S., Bordallo, M., Boutellaa, E., Peng, J., Feng, X., & Hadid, A. (2016). On pain assessment from facial videos using spatio-temporal local descriptors. In 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/IPTA.2016.7820930
Yang, Ruijing ; Tong, Shujun ; Bordallo, Miguel ; Boutellaa, Elhocine ; Peng, Jinye ; Feng, Xiaoyi ; Hadid, Abdenour. / On pain assessment from facial videos using spatio-temporal local descriptors. 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). Institute of Electrical and Electronic Engineers IEEE, 2016.
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abstract = "Automatically recognizing pain from spontaneous facial expression is of increased attention, since it can provide for a direct and relatively objective indication to pain experience. Until now, most of the existing works have focused on analyzing pain from individual images or video-frames, hence discarding the spatio-temporal information that can be useful in the continuous assessment of pain. In this context, this paper investigates and quantifies for the first time the role of the spatio-temporal information in pain assessment by comparing the performance of several baseline local descriptors used in their traditional spatial form against their spatio-temporal counterparts that take into account the video dynamics. For this purpose, we perform extensive experiments on two benchmark datasets. Our results indicate that using spatio-temporal information to classify video-sequences consistently shows superior performance when compared against the one obtained using only static information.",
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Yang, R, Tong, S, Bordallo, M, Boutellaa, E, Peng, J, Feng, X & Hadid, A 2016, On pain assessment from facial videos using spatio-temporal local descriptors. in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/IPTA.2016.7820930

On pain assessment from facial videos using spatio-temporal local descriptors. / Yang, Ruijing; Tong, Shujun; Bordallo, Miguel; Boutellaa, Elhocine; Peng, Jinye; Feng, Xiaoyi; Hadid, Abdenour.

2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). Institute of Electrical and Electronic Engineers IEEE, 2016.

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

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T1 - On pain assessment from facial videos using spatio-temporal local descriptors

AU - Yang, Ruijing

AU - Tong, Shujun

AU - Bordallo, Miguel

AU - Boutellaa, Elhocine

AU - Peng, Jinye

AU - Feng, Xiaoyi

AU - Hadid, Abdenour

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N2 - Automatically recognizing pain from spontaneous facial expression is of increased attention, since it can provide for a direct and relatively objective indication to pain experience. Until now, most of the existing works have focused on analyzing pain from individual images or video-frames, hence discarding the spatio-temporal information that can be useful in the continuous assessment of pain. In this context, this paper investigates and quantifies for the first time the role of the spatio-temporal information in pain assessment by comparing the performance of several baseline local descriptors used in their traditional spatial form against their spatio-temporal counterparts that take into account the video dynamics. For this purpose, we perform extensive experiments on two benchmark datasets. Our results indicate that using spatio-temporal information to classify video-sequences consistently shows superior performance when compared against the one obtained using only static information.

AB - Automatically recognizing pain from spontaneous facial expression is of increased attention, since it can provide for a direct and relatively objective indication to pain experience. Until now, most of the existing works have focused on analyzing pain from individual images or video-frames, hence discarding the spatio-temporal information that can be useful in the continuous assessment of pain. In this context, this paper investigates and quantifies for the first time the role of the spatio-temporal information in pain assessment by comparing the performance of several baseline local descriptors used in their traditional spatial form against their spatio-temporal counterparts that take into account the video dynamics. For this purpose, we perform extensive experiments on two benchmark datasets. Our results indicate that using spatio-temporal information to classify video-sequences consistently shows superior performance when compared against the one obtained using only static information.

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Yang R, Tong S, Bordallo M, Boutellaa E, Peng J, Feng X et al. On pain assessment from facial videos using spatio-temporal local descriptors. In 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). Institute of Electrical and Electronic Engineers IEEE. 2016 https://doi.org/10.1109/IPTA.2016.7820930