The Delaware Pain Database: a set of painful expressions and corresponding norming data

Mende-Siedlecki, Peter; Qu-Lee, Jennie; Lin, Jingrun; Drain, Alexis; Goharzad, Azaadeh

doi:10.1097/pr9.0000000000000853

Cited by 24 publications

(33 citation statements)

References 82 publications

(109 reference statements)

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“…Emotion expressions were created following procedures detailed in the Delaware Pain Database (Mende-Siedlecki et al, 2020). In Experiment 1, participants saw six Black and six White targets making six different expressions of pain and anger.…”

Section: Methodsmentioning

confidence: 99%

Seeing no pain: Assessing the generalizability of racial bias in pain perception.

Mende-Siedlecki¹,

Lin²,

Ferron³

et al. 2021

Emotion

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Racial disparities in pain care may stem, in part, from perceptual roots. It remains unresolved, however, whether this perceptual gap is driven by general deficits in intergroup emotion recognition, endorsement of specific racial stereotypes, or an interaction between the two. We conducted four experiments (total N = 635) assessing relationships between biases in pain perception and treatment and biases in the perception of anger, happiness, fear, and sadness. Participants saw Black and White male targets making increasingly painful and angry (Experiment 1), happy (Experiment 2), fearful (Experiment 3), or sad expressions (Experiment 4). The effect of target race consistently varied based on the emotion displayed. Participants repeatedly saw pain more readily on White (vs. Black) male faces. However, while participants also saw sadness less readily on Black faces, perception of anger, fear, and happiness did not vary by target race. Moreover, the tendency to see pain less readily on Black faces predicted similar differences in recognizing (particularly negative) expressions, though only racial bias in pain perception facilitated similar biases in treatment. Finally, while endorsement of racialized threat stereotypes facilitated recognition of angry expressions and was marginally associated with impeded recognition of happy expressions on Black faces, gaps in pain perception were not reliably related to stereotype endorsement. These data suggest that while racial bias in pain perception is associated with a general bias in recognizing negative emotion on Black male faces, the effects of target race on pain perception are particularly robust and have distinct consequences for gaps in treatment.

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Section: Methodsmentioning

confidence: 99%

Seeing no pain: Assessing the generalizability of racial bias in pain perception.

Mende-Siedlecki¹,

Lin²,

Ferron³

et al. 2021

Emotion

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show abstract

“…The stimulus materials (ie, avatars) were taken from the empirically validated Delaware Pain Database (DPD 29 ). To create these stimuli, photographs were taken of real Black and White adult faces (age range: 18-35 years).…”

Section: Methodsmentioning

confidence: 99%

“…We selected 12 avatars that did not differ on social evaluations, latent emotional content, and demographic features (apart from their perceived race and gender). 29…”

Section: Methodsmentioning

confidence: 99%

Racial disparities in observers' attention to and estimations of others' pain

Kissi

Ryckeghem

Mende-Siedlecki

et al. 2021

Pain

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“…Further, there are some limitations with UNBC-McMaster, such as the small number of participants (subjects) which may lead to a database bias problem. Movement also varies across subjects during pain stimulation, and their faces are sometimes partially occluded and this represents potential confounds in experimental designs [27]. Gruss et al [28] reported significant progress in automated pain assessment when using biopotential data.…”

Section: Related Workmentioning

confidence: 99%

Automatic vs. Human Recognition of Pain Intensity from Facial Expression on the X-ITE Pain Database

Othman

Werner

Saxen

et al. 2021

Sensors

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Prior work on automated methods demonstrated that it is possible to recognize pain intensity from frontal faces in videos, while there is an assumption that humans are very adept at this task compared to machines. In this paper, we investigate whether such an assumption is correct by comparing the results achieved by two human observers with the results achieved by a Random Forest classifier (RFc) baseline model (called RFc-BL) and by three proposed automated models. The first proposed model is a Random Forest classifying descriptors of Action Unit (AU) time series; the second is a modified MobileNetV2 CNN classifying face images that combine three points in time; and the third is a custom deep network combining two CNN branches using the same input as for MobileNetV2 plus knowledge of the RFc. We conduct experiments with X-ITE phasic pain database, which comprises videotaped responses to heat and electrical pain stimuli, each of three intensities. Distinguishing these six stimulation types plus no stimulation was the main 7-class classification task for the human observers and automated approaches. Further, we conducted reduced 5-class and 3-class classification experiments, applied Multi-task learning, and a newly suggested sample weighting method. Experimental results show that the pain assessments of the human observers are significantly better than guessing and perform better than the automatic baseline approach (RFc-BL) by about 1%; however, the human performance is quite poor due to the challenge that pain that is ethically allowed to be induced in experimental studies often does not show up in facial reaction. We discovered that downweighting those samples during training improves the performance for all samples. The proposed RFc and two-CNNs models (using the proposed sample weighting) significantly outperformed the human observer by about 6% and 7%, respectively.

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The Delaware Pain Database: a set of painful expressions and corresponding norming data

Abstract: Supplemental Digital Content is Available in the Text. We present a fully characterized face stimulus database focusing on expressions of pain, surpassing previous sets in terms of size, quality, diversity, and characterization.

Cited by 24 publications

References 82 publications

Seeing no pain: Assessing the generalizability of racial bias in pain perception.

Seeing no pain: Assessing the generalizability of racial bias in pain perception.

Racial disparities in observers' attention to and estimations of others' pain

Automatic vs. Human Recognition of Pain Intensity from Facial Expression on the X-ITE Pain Database

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