Padova Emotional Dataset of Facial Expressions (PEDFE): A unique dataset of genuine and posed emotional facial expressions

Abstract

Facial expressions are among the most powerful signals for human beings to convey their emotional states. Indeed, emotional facial datasets represent the most effective and controlled method of examining humans’ interpretation of and reaction to various emotions. However, scientific research on emotion mainly relied on static pictures of facial expressions posed (i.e., simulated) by actors, creating a significant bias in emotion literature. This dataset tries to fill this gap, providing a considerable amount (N = 1458) of dynamic genuine (N = 707) and posed (N = 751) clips of the six universal emotions from 56 participants. The dataset is available in two versions: original clips, including participants’ body and background, and modified clips, where only the face of participants is visible. Notably, the original dataset has been validated by 122 human raters, while the modified dataset has been validated by 280 human raters. Hit rates for emotion and genuineness, as well as the mean, standard deviation of genuineness, and intensity perception, are provided for each clip to allow future users to select the most appropriate clips needed to answer their scientific questions.

What is missing in the study of emotion expression?

“…Facial displays are not identical for different subjects, nor even for the same emotion ( Sadeghi et al, 2013 ; Sangineto et al, 2014 ; Durán et al, 2017 ). A recent paper ( Cardaioli et al, 2022 ) capitalized the PEDFE dataset described above ( Miolla et al, 2022 ). The PEDFE dataset is unique to explore inter-individual differences in emotional expression, as, besides including genuine and posed dynamic emotional expression, it also includes many emotional stimuli for each “actor,” where the same emotion is expressed with different intensities or response to different stimuli.…”

“…And even when it was verified, it missed the next step, which was to cross-reference the observers’ scoring with the emotion actually experienced by the actor ( Dawel et al, 2017 ). Only recently, a dataset of authentic and inauthentic emotional expressions matched the emotion felt by the actor with that perceived by the observer in terms of intensity and genuineness ( Miolla et al, 2022 ). The next step will be to create dataset including also the context of the emotional display.…”

The Spatiotemporal Dynamics of Facial Movements Reveals the Left Side of a Posed Smile

“…No instruction whatsoever was given on the duration of the expression. This procedure was aimed at generating expressions without forcing the participants to respect time constraints as in the Spontaneous conditions [ 40 ]. To avoid possible carry-over effects between trials due to the Emotional Induction from the videos used in the Spontaneous condition, we capitalized on the procedure adopted by Sowden and colleagues [ 35 ], and divided the trials into two separate blocks (first the spontaneous block, then the posed block after a brief pause).…”

“…For the Spontaneous condition, we selected N = 2 emotion-inducing videos from a recently-validated dataset structured to elicit genuine facial expressions [ 40 ]. Videoclips were extracted from popular comedy movies in which actors produced hilarity without showing smiling faces (e.g., jokes by professional comedians).…”