Automatic Detection of Pain from Facial Expressions: A Survey

Hassan T, Seuss D, Wollenberg J, Weitz K, Kunz M, Lautenbacher S, Garbas J-U, Schmid U (2021)
IEEE Transactions on Pattern Analysis and Machine Intelligence 43(6): 1815-1831.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Hassan, TeenaUniBi; Seuss, Dominik; Wollenberg, Johannes; Weitz, Katharina; Kunz, Miriam; Lautenbacher, Stefan; Garbas, Jens-Uwe; Schmid, Ute
Abstract / Bemerkung
Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon. Observer reports of pain could be prone to errors due to subjective biases of observers. Moreover, continuous monitoring by humans is impractical. Therefore, automatic pain detection technology could be deployed to assist human caregivers and complement their service, thereby improving the quality of pain management, especially for noncommunicative patients. Facial expressions are a reliable indicator of pain, and are used in all observer-based pain assessment tools. Following the advancements in automatic facial expression analysis, computer vision researchers have tried to use this technology for developing approaches for automatically detecting pain from facial expressions. This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions. The survey covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and finally, the machine learning methods used.
Stichworte
Pain; Feature extraction; Task analysis; Imaging; Encoding; Observers; Machine learning; Automatic pain detection; facial expressions of pain; pain datasets; pain feature representation; facial expression analysis; machine learning; survey
Erscheinungsjahr
2021
Zeitschriftentitel
IEEE Transactions on Pattern Analysis and Machine Intelligence
Band
43
Ausgabe
6
Seite(n)
1815-1831
ISSN
0162-8828
eISSN
1939-3539
Page URI
https://pub.uni-bielefeld.de/record/2955349

Zitieren

Hassan T, Seuss D, Wollenberg J, et al. Automatic Detection of Pain from Facial Expressions: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence . 2021;43(6):1815-1831.
Hassan, T., Seuss, D., Wollenberg, J., Weitz, K., Kunz, M., Lautenbacher, S., Garbas, J. - U., et al. (2021). Automatic Detection of Pain from Facial Expressions: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence , 43(6), 1815-1831. https://doi.org/10.1109/TPAMI.2019.2958341
Hassan, T., Seuss, D., Wollenberg, J., Weitz, K., Kunz, M., Lautenbacher, S., Garbas, J. - U., and Schmid, U. (2021). Automatic Detection of Pain from Facial Expressions: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 1815-1831.
Hassan, T., et al., 2021. Automatic Detection of Pain from Facial Expressions: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence , 43(6), p 1815-1831.
T. Hassan, et al., “Automatic Detection of Pain from Facial Expressions: A Survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, 2021, pp. 1815-1831.
Hassan, T., Seuss, D., Wollenberg, J., Weitz, K., Kunz, M., Lautenbacher, S., Garbas, J.-U., Schmid, U.: Automatic Detection of Pain from Facial Expressions: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence . 43, 1815-1831 (2021).
Hassan, Teena, Seuss, Dominik, Wollenberg, Johannes, Weitz, Katharina, Kunz, Miriam, Lautenbacher, Stefan, Garbas, Jens-Uwe, and Schmid, Ute. “Automatic Detection of Pain from Facial Expressions: A Survey”. IEEE Transactions on Pattern Analysis and Machine Intelligence 43.6 (2021): 1815-1831.

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