Pain Detection From Facial Expressions Based on Transformers and Distillation

Morabit, Safaa El; Rivenq, Atika

doi:10.1109/isivc54825.2022.9800746

Cited by 7 publications

References 22 publications

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Decoding pain: prediction under different emotional contexts through physiological signals

Alves,

Brás,

Sebastião

2024

Int J Data Sci Anal

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Pain recognition is a challenging yet crucial problem. Common methods used to measure pain are based on self-reported tools; however, not every person can communicate it. Therefore, automatic pain detection methods have emerged. Since pain is related to the emotional state of an individual, this variable must be considered. Thus, this work proposes pain prediction under different emotional contexts. For this purpose, data were collected during a protocol designed for pain induction with previous emotional elicitation. Emotions were elicited through videos composed of excerpts of documentaries, horror and comedy films, while the pain was induced through a Cold Pressor Test. Physiological signals, such as electrocardiogram, electrodermal activity and surface electromyogram, were collected during the protocol. Furthermore, several questionnaires were answered and pain reports were also registered. Two problems were addressed: pain classification and estimation of the Pain Tolerance score. The algorithm with the best performance for each problem was found using only data from the neutral session and nested cross-validation strategy. Using only physiological data from the neutral session, a F1-score of 99.32% was obtained for pain recognition and a mean absolute error (MAE) of 0.29 was obtained for Pain Tolerance estimation. When considering all the emotional sessions, the physiological data were merged with scores of the Visual Analogue Scale questionnaire, achieving a F1-score of 98.60% and a MAE of 0.41, for the first and second problems, respectively. These results are promising and stress out the key role that the emotional context of the individuals plays in pain prediction.

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Decoding pain: prediction under different emotional contexts through physiological signals

Alves,

Brás,

Sebastião

2024

Int J Data Sci Anal

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Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images

Barua

Baygin

Doğan

et al. 2022

Sci Rep

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Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or “shutter blinds”. A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases—University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database—which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.

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