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Abstract
A large number of publications have focused on the study of pain expressions. Despite the growing knowledge, the availability of pain-related face databases is still very scarce compared with other emotional facial expressions. The Pain E-Motion Faces Database (PEMF) is a new open-access database currently consisting of 272 micro-clips of 68 different identities. Each model displays one neutral expression and three pain-related facial expressions: posed, spontaneous-algometer and spontaneous-CO2 laser. Normative ratings of pain intensity, valence and arousal were provided by students of three different European universities. Six independent coders carried out a coding process on the facial stimuli based on the Facial Action Coding System (FACS), in which ratings of intensity of pain, valence and arousal were computed for each type of facial expression. Gender and age effects of models across each type of micro-clip were also analysed. Additionally, participants' ability to discriminate the veracity of pain-related facial expressions (i.e., spontaneous vs posed) was explored. Finally, a series of ANOVAs were carried out to test the presence of other basic emotions and common facial action unit (AU) patterns. The main results revealed that posed facial expressions received higher ratings of pain intensity, more negative valence and higher arousal compared with spontaneous pain-related and neutral faces. No differential effects of model gender were found. Participants were unable to accurately discriminate whether a given pain-related face represented spontaneous or posed pain. PEMF thus constitutes a large open-source and reliable set of dynamic pain expressions useful for designing experimental studies focused on pain processes.
Keywords
FACIAL EXPRESSIONS, SEX-DIFFERENCES, RECOGNITION, COMMUNICATION, UNIVERSALS, ATTENTION, BEHAVIOR, GENUINE, Pain-related faces, Emotional expressions, Database

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MLA
Fernandes-Magalhaes, Roberto, et al. “Pain E-Motion Faces Database (PEMF) : Pain-Related Micro-Clips for Emotion Research.” BEHAVIOR RESEARCH METHODS, 2023, doi:10.3758/s13428-022-01992-4.
APA
Fernandes-Magalhaes, R., Carpio, A., Ferrera, D., Van Ryckeghem, D., Pelaez, I., Barjola, P., … Mercado, F. (2023). Pain E-motion Faces Database (PEMF) : pain-related micro-clips for emotion research. BEHAVIOR RESEARCH METHODS. https://doi.org/10.3758/s13428-022-01992-4
Chicago author-date
Fernandes-Magalhaes, Roberto, Alberto Carpio, David Ferrera, Dimitri Van Ryckeghem, Irene Pelaez, Paloma Barjola, Maria Eugenia De Lahoz, et al. 2023. “Pain E-Motion Faces Database (PEMF) : Pain-Related Micro-Clips for Emotion Research.” BEHAVIOR RESEARCH METHODS. https://doi.org/10.3758/s13428-022-01992-4.
Chicago author-date (all authors)
Fernandes-Magalhaes, Roberto, Alberto Carpio, David Ferrera, Dimitri Van Ryckeghem, Irene Pelaez, Paloma Barjola, Maria Eugenia De Lahoz, Maria Carmen Martin-Buro, Jose Antonio Hinojosa, Stefaan Van Damme, Luis Carretie, and Francisco Mercado. 2023. “Pain E-Motion Faces Database (PEMF) : Pain-Related Micro-Clips for Emotion Research.” BEHAVIOR RESEARCH METHODS. doi:10.3758/s13428-022-01992-4.
Vancouver
1.
Fernandes-Magalhaes R, Carpio A, Ferrera D, Van Ryckeghem D, Pelaez I, Barjola P, et al. Pain E-motion Faces Database (PEMF) : pain-related micro-clips for emotion research. BEHAVIOR RESEARCH METHODS. 2023;
IEEE
[1]
R. Fernandes-Magalhaes et al., “Pain E-motion Faces Database (PEMF) : pain-related micro-clips for emotion research,” BEHAVIOR RESEARCH METHODS, 2023.
@article{8772066,
  abstract     = {{A large number of publications have focused on the study of pain expressions. Despite the growing knowledge, the availability of pain-related face databases is still very scarce compared with other emotional facial expressions. The Pain E-Motion Faces Database (PEMF) is a new open-access database currently consisting of 272 micro-clips of 68 different identities. Each model displays one neutral expression and three pain-related facial expressions: posed, spontaneous-algometer and spontaneous-CO2 laser. Normative ratings of pain intensity, valence and arousal were provided by students of three different European universities. Six independent coders carried out a coding process on the facial stimuli based on the Facial Action Coding System (FACS), in which ratings of intensity of pain, valence and arousal were computed for each type of facial expression. Gender and age effects of models across each type of micro-clip were also analysed. Additionally, participants' ability to discriminate the veracity of pain-related facial expressions (i.e., spontaneous vs posed) was explored. Finally, a series of ANOVAs were carried out to test the presence of other basic emotions and common facial action unit (AU) patterns. The main results revealed that posed facial expressions received higher ratings of pain intensity, more negative valence and higher arousal compared with spontaneous pain-related and neutral faces. No differential effects of model gender were found. Participants were unable to accurately discriminate whether a given pain-related face represented spontaneous or posed pain. PEMF thus constitutes a large open-source and reliable set of dynamic pain expressions useful for designing experimental studies focused on pain processes.}},
  author       = {{Fernandes-Magalhaes, Roberto and Carpio, Alberto and Ferrera, David and Van Ryckeghem, Dimitri and Pelaez, Irene and Barjola, Paloma and De Lahoz, Maria Eugenia and Martin-Buro, Maria Carmen and Hinojosa, Jose Antonio and Van Damme, Stefaan and Carretie, Luis and Mercado, Francisco}},
  issn         = {{1554-351X}},
  journal      = {{BEHAVIOR RESEARCH METHODS}},
  keywords     = {{FACIAL EXPRESSIONS,SEX-DIFFERENCES,RECOGNITION,COMMUNICATION,UNIVERSALS,ATTENTION,BEHAVIOR,GENUINE,Pain-related faces,Emotional expressions,Database}},
  language     = {{eng}},
  pages        = {{14}},
  title        = {{Pain E-motion Faces Database (PEMF) : pain-related micro-clips for emotion research}},
  url          = {{http://doi.org/10.3758/s13428-022-01992-4}},
  year         = {{2023}},
}

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