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

• Main Authors: Peter Mende-Siedlecki, Jennie Qu-Lee, Jingrun Lin, Alexis Drain, Azaadeh Goharzad
• Format: Article
• Language: English
• Published: Wolters Kluwer 2020-12-01
• Series: PAIN Reports
• Online Access: http://journals.lww.com/painrpts/fulltext/10.1097/PR9.0000000000000853

Abstract. Introduction:. Facial expressions of pain serve an essential social function by communicating suffering and soliciting aid. Accurate visual perception of painful expressions is critical because the misperception of pain signals can have serious clinical and social consequences. Therefore, it is essential that researchers have access to high-quality, diverse databases of painful expressions to better understand accuracy and bias in pain perception. Objectives:. This article describes the development of a large-scale face stimulus database focusing on expressions of pain. Methods:. We collected and normed a database of images of models posing painful facial expressions. We also characterized these stimuli in terms of the presence of a series of pain-relevant facial action units. In addition to our primary database of posed expressions, we provide a separate database of computer-rendered expressions of pain that may be applied to any neutral face photograph. Results:. The resulting database comprises 229 unique (and now publicly available) painful expressions. To the best of our knowledge, there are no existing databases of this size, quality, or diversity in terms of race, gender, and expression intensity. We provide evidence for the reliability of expressions and evaluations of pain within these stimuli, as well as a full characterization of this set along dimensions relevant to pain such as perceived status, strength, and dominance. Moreover, our second database complements the primary set in terms of experimental control and precision. Conclusion:. These stimuli will facilitate reproducible research in both experimental and clinical domains into the mechanisms supporting accuracy and bias in pain perception and care.