Continuous pain intensity estimation from facial expressions

Sebastian Kaltwang; Ognjen Rudovic; Maja Pantic

doi:10.1007/978-3-642-33191-6_36

Continuous pain intensity estimation from facial expressions

Sebastian Kaltwang, Ognjen Rudovic, Maja Pantic

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

209 Citations (Scopus)

28 Downloads (Pure)

Abstract

Automatic pain recognition is an evolving research area with promising applications in health care. In this paper, we propose the first fully automatic approach to continuous pain intensity estimation from facial images. We first learn a set of independent regression functions for continuous pain intensity estimation using different shape (facial landmarks) and appearance (DCT and LBP) features, and then perform their late fusion. We show on the recently published UNBC-MacMaster Shoulder Pain Expression Archive Database that late fusion of the afore-mentioned features leads to better pain intensity estimation compared to feature-specific pain intensity estimation.

Original language	English
Title of host publication	Advances in Visual Computing
Subtitle of host publication	8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers
Editors	George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	368-377
Number of pages	10
Volume	Part II
ISBN (Electronic)	978-3-642-33191-6
ISBN (Print)	978-3-642-33190-9
DOIs	https://doi.org/10.1007/978-3-642-33191-6_36
Publication status	Published - 16 Jul 2012
Event	8th International Symposium on Visual Computing, ISVC 2012 - Crete, Greece Duration: 16 Jul 2012 → 18 Jul 2012 Conference number: 8

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	7432
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Symposium on Visual Computing, ISVC 2012
Abbreviated title	ISVC
Country/Territory	Greece
City	Crete
Period	16/07/12 → 18/07/12

Keywords

EWI-22978
METIS-296265
IR-84318
HMI-MI: MULTIMODAL INTERACTIONS

Access to Document

10.1007/978-3-642-33191-6_36

Cite this

Kaltwang, S., Rudovic, O., & Pantic, M. (2012). Continuous pain intensity estimation from facial expressions. In G. Bebis, R. Boyle, B. Parvin, & D. Koracin (Eds.), Advances in Visual Computing: 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers (Vol. Part II, pp. 368-377). (Lecture Notes in Computer Science; Vol. 7432). Springer. https://doi.org/10.1007/978-3-642-33191-6_36

Kaltwang, Sebastian ; Rudovic, Ognjen ; Pantic, Maja. / Continuous pain intensity estimation from facial expressions. Advances in Visual Computing: 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers. editor / George Bebis ; Richard Boyle ; Bahram Parvin ; Darko Koracin. Vol. Part II Berlin, Germany : Springer, 2012. pp. 368-377 (Lecture Notes in Computer Science).

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title = "Continuous pain intensity estimation from facial expressions",

abstract = "Automatic pain recognition is an evolving research area with promising applications in health care. In this paper, we propose the first fully automatic approach to continuous pain intensity estimation from facial images. We first learn a set of independent regression functions for continuous pain intensity estimation using different shape (facial landmarks) and appearance (DCT and LBP) features, and then perform their late fusion. We show on the recently published UNBC-MacMaster Shoulder Pain Expression Archive Database that late fusion of the afore-mentioned features leads to better pain intensity estimation compared to feature-specific pain intensity estimation.",

keywords = "EWI-22978, METIS-296265, IR-84318, HMI-MI: MULTIMODAL INTERACTIONS",

author = "Sebastian Kaltwang and Ognjen Rudovic and Maja Pantic",

year = "2012",

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doi = "10.1007/978-3-642-33191-6_36",

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volume = "Part II",

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editor = "George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin",

booktitle = "Advances in Visual Computing",

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note = "8th International Symposium on Visual Computing, ISVC 2012, ISVC ; Conference date: 16-07-2012 Through 18-07-2012",

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Kaltwang, S, Rudovic, O & Pantic, M 2012, Continuous pain intensity estimation from facial expressions. in G Bebis, R Boyle, B Parvin & D Koracin (eds), Advances in Visual Computing: 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers. vol. Part II, Lecture Notes in Computer Science, vol. 7432, Springer, Berlin, Germany, pp. 368-377, 8th International Symposium on Visual Computing, ISVC 2012, Crete, Greece, 16/07/12. https://doi.org/10.1007/978-3-642-33191-6_36

Continuous pain intensity estimation from facial expressions. / Kaltwang, Sebastian; Rudovic, Ognjen; Pantic, Maja.
Advances in Visual Computing: 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers. ed. / George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin. Vol. Part II Berlin, Germany: Springer, 2012. p. 368-377 (Lecture Notes in Computer Science; Vol. 7432).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - Automatic pain recognition is an evolving research area with promising applications in health care. In this paper, we propose the first fully automatic approach to continuous pain intensity estimation from facial images. We first learn a set of independent regression functions for continuous pain intensity estimation using different shape (facial landmarks) and appearance (DCT and LBP) features, and then perform their late fusion. We show on the recently published UNBC-MacMaster Shoulder Pain Expression Archive Database that late fusion of the afore-mentioned features leads to better pain intensity estimation compared to feature-specific pain intensity estimation.

AB - Automatic pain recognition is an evolving research area with promising applications in health care. In this paper, we propose the first fully automatic approach to continuous pain intensity estimation from facial images. We first learn a set of independent regression functions for continuous pain intensity estimation using different shape (facial landmarks) and appearance (DCT and LBP) features, and then perform their late fusion. We show on the recently published UNBC-MacMaster Shoulder Pain Expression Archive Database that late fusion of the afore-mentioned features leads to better pain intensity estimation compared to feature-specific pain intensity estimation.

KW - EWI-22978

KW - METIS-296265

KW - IR-84318

KW - HMI-MI: MULTIMODAL INTERACTIONS

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DO - 10.1007/978-3-642-33191-6_36

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A2 - Boyle, Richard

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Kaltwang S, Rudovic O, Pantic M. Continuous pain intensity estimation from facial expressions. In Bebis G, Boyle R, Parvin B, Koracin D, editors, Advances in Visual Computing: 8th International Symposium, ISVC 2012, Rethymnon, Crete, Greece, July 16-18, 2012, Revised Selected Papers. Vol. Part II. Berlin, Germany: Springer. 2012. p. 368-377. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-33191-6_36

Continuous pain intensity estimation from facial expressions

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