Spatiotemporal Facial Super-Pixels for Pain Detection

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Abstract

Pain detection using facial images is of critical importance in many eHealth applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBC- McMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios.
Original languageEnglish
Title of host publicationIX Conference on Articulated Motion and Deformable Objects
EditorsFrancisco José Perales, Josef Kittler
Place of PublicationSpain
PublisherSpringer
Publication date2 Jul 2016
Pages34-43
DOIs
Publication statusPublished - 2 Jul 2016
EventIX Conference on Articulated Motion and Deformable Objects - Mallorca, Spain
Duration: 13 Jul 201615 Jul 2016
http://amdo2016.uib.es/

Conference

ConferenceIX Conference on Articulated Motion and Deformable Objects
CountrySpain
CityMallorca
Period13/07/201615/07/2016
Internet address
SeriesLecture Notes in Computer Science
Volume9756
ISSN0302-9743

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Keywords

  • facial images
  • super-pixels
  • spatiotemporal filters
  • pain detection

Cite this

Lundtoft, D. H., Nasrollahi, K., Moeslund, T. B., & Guerrero, S. E. (2016). Spatiotemporal Facial Super-Pixels for Pain Detection. In F. J. Perales, & J. Kittler (Eds.), IX Conference on Articulated Motion and Deformable Objects (pp. 34-43). Spain: Springer. Lecture Notes in Computer Science, Vol.. 9756 https://doi.org/10.1007/978-3-319-41778-3_4
Lundtoft, Dennis Holm ; Nasrollahi, Kamal ; Moeslund, Thomas B. ; Guerrero, Sergio Escalera. / Spatiotemporal Facial Super-Pixels for Pain Detection. IX Conference on Articulated Motion and Deformable Objects. editor / Francisco José Perales ; Josef Kittler. Spain : Springer, 2016. pp. 34-43 (Lecture Notes in Computer Science, Vol. 9756).
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title = "Spatiotemporal Facial Super-Pixels for Pain Detection",
abstract = "Pain detection using facial images is of critical importance in many eHealth applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBC- McMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios.",
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Lundtoft, DH, Nasrollahi, K, Moeslund, TB & Guerrero, SE 2016, Spatiotemporal Facial Super-Pixels for Pain Detection. in FJ Perales & J Kittler (eds), IX Conference on Articulated Motion and Deformable Objects. Springer, Spain, Lecture Notes in Computer Science, vol. 9756, pp. 34-43, IX Conference on Articulated Motion and Deformable Objects, Mallorca, Spain, 13/07/2016. https://doi.org/10.1007/978-3-319-41778-3_4

Spatiotemporal Facial Super-Pixels for Pain Detection. / Lundtoft, Dennis Holm; Nasrollahi, Kamal; Moeslund, Thomas B.; Guerrero, Sergio Escalera.

IX Conference on Articulated Motion and Deformable Objects. ed. / Francisco José Perales; Josef Kittler. Spain : Springer, 2016. p. 34-43 (Lecture Notes in Computer Science, Vol. 9756).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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N2 - Pain detection using facial images is of critical importance in many eHealth applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBC- McMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios.

AB - Pain detection using facial images is of critical importance in many eHealth applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBC- McMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios.

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Lundtoft DH, Nasrollahi K, Moeslund TB, Guerrero SE. Spatiotemporal Facial Super-Pixels for Pain Detection. In Perales FJ, Kittler J, editors, IX Conference on Articulated Motion and Deformable Objects. Spain: Springer. 2016. p. 34-43. (Lecture Notes in Computer Science, Vol. 9756). https://doi.org/10.1007/978-3-319-41778-3_4