The efficiency of dynamic and static facial expression recognition

Gold, Jason M.; Barker, Jarrett D.; Barr, Shawn; Bittner, Jennifer; Bromfield, W. Drew; Chu, Nicole; Goode, Roy A.; Lee, Doori; Simmons, Michael; Srinath, Aparna

doi:10.1167/13.5.23

Cited by 39 publications

(72 citation statements)

References 27 publications

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“…The lack of consistency across studies may also result from the nature of the stimulus materials used. Although some studies have used stimuli based on real human models (actors or nonactors), others included avatars or computer edited faces (e.g., Cigna et al, 2015;Gold et al, 2013;Horstmann & Ansorge, 2009). Yet some authors (e.g., Sato, Fujimura, & Suzuki, 2008) suggest that the use of Breal people^is more suitable when using dynamic stimuli.…”

Section: Static Versus Dynamic Facial Expressionsmentioning

confidence: 99%

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

et al. 2016

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This article presents subjective rating norms for a new set of Stills And Videos of facial Expressions-the SAVE database. Twenty nonprofessional models were filmed while posing in three different facial expressions (smile, neutral, and frown). After each pose, the models completed the PANAS questionnaire, and reported more positive affect after smiling and more negative affect after frowning. From the shooting material, stills and 5 s and 10 s videos were edited (total stimulus set = 180). A different sample of 120 participants evaluated the stimuli for attractiveness, arousal, clarity, genuineness, familiarity, intensity, valence, and similarity. Overall, facial expression had a main effect in all of the evaluated dimensions, with smiling models obtaining the highest ratings. Frowning expressions were perceived as being more arousing, clearer, and more intense, but also as more negative than neutral expressions. Stimulus presentation format only influenced the ratings of attractiveness, familiarity, genuineness, and intensity. The attractiveness and familiarity ratings increased with longer exposure times, whereas genuineness decreased. The ratings in the several dimensions were correlated. The subjective norms of facial stimuli presented in this article have potential applications to the work of researchers in several research domains. From our database, researchers may choose the most adequate stimulus presentation format for a particular experiment, select and manipulate the dimensions of interest, and control for the remaining dimensions. The full stimulus set and descriptive results (means, standard deviations, and confidence intervals) for each stimulus per dimension are provided as supplementary material.

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Section: Static Versus Dynamic Facial Expressionsmentioning

confidence: 99%

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

et al. 2016

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“…Thirdly, although we utilised a dynamic emotional expression task to increase the ecological validity of the task, some studies have suggested that the dynamic facial movements actually play only a small role in the ability to identify emotion from facial expressions (Gold et al, 2013). Nonetheless, employing different variants of facial emotion stimuli develops our understanding of the robustness or otherwise of any effect irrespective of an impact on ecological validity.…”

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confidence: 99%

Processing of Facial Emotion in Bipolar Depression and Euthymia

Robinson

Gray

Burt

et al. 2015

J Int Neuropsychol Soc

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Previous studies of facial emotion processing in bipolar disorder (BD) have reported conflicting findings. In independently conducted studies, we investigate facial emotion labeling in euthymic and depressed BD patients using tasks with static and dynamically morphed images of different emotions displayed at different intensities. Study 1 included 38 euthymic BD patients and 28 controls. Participants completed two tasks: labeling of static images of basic facial emotions (anger, disgust, fear, happy, sad) shown at different expression intensities; the Eyes Test (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001), which involves recognition of complex emotions using only the eye region of the face. Study 2 included 53 depressed BD patients and 47 controls. Participants completed two tasks: labeling of “dynamic” facial expressions of the same five basic emotions; the Emotional Hexagon test (Young, Perret, Calder, Sprengelmeyer, & Ekman, 2002). There were no significant group differences on any measures of emotion perception/labeling, compared to controls. A significant group by intensity interaction was observed in both emotion labeling tasks (euthymia and depression), although this effect did not survive the addition of measures of executive function/psychomotor speed as covariates. Only 2.6–15.8% of euthymic patients and 7.8–13.7% of depressed patients scored below the 10th percentile of the controls for total emotion recognition accuracy. There was no evidence of specific deficits in facial emotion labeling in euthymic or depressed BD patients. Methodological variations—including mood state, sample size, and the cognitive demands of the tasks—may contribute significantly to the variability in findings between studies. (JINS, 2015, 21, 709–721)

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“…In addition, it would also be interesting to clarify whether deaf adults would benefit from the presentation of dynamic emotional expressions over static images. We very recently showed that the performance for the recognition of static as compared to dynamic expressions was notably less effective in fragile face processing systems, such as brain-damaged patients (Richoz, Jack, Garrod, Schyns, & Caldara, 2015) and elderly adults (Richoz, Lao, Pascalis, & Caldara, 2018) by using a database of stimuli controlled for the amount of low-level information carried over time (Gold et al, 2013). As sign language is dynamic, we could expect similar results in the deaf population, a question that needs to be addressed in a future study.…”

Section: Discussionmentioning

confidence: 81%

Quantifying Facial Expression Intensity and Signal Use in Deaf Signers

Stoll

Rodger

Lao

et al. 2019

The Journal of Deaf Studies and Deaf Education

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We live in a world of rich dynamic multisensory signals. Hearing individuals rapidly and effectively integrate multimodal signals to decode biologically relevant facial expressions of emotion. Yet, it remains unclear how facial expressions are decoded by deaf adults in the absence of an auditory sensory channel. We thus compared early and profoundly deaf signers (n = 46) with hearing nonsigners (n = 48) on a psychophysical task designed to quantify their recognition performance for the six basic facial expressions of emotion. Using neutral-to-expression image morphs and noise-to-full signal images, we quantified the intensity and signal levels required by observers to achieve expression recognition. Using Bayesian modeling, we found that deaf observers require more signal and intensity to recognize disgust, while reaching comparable performance for the remaining expressions. Our results provide a robust benchmark for the intensity and signal use in deafness and novel insights into the differential coding of facial expressions of emotion between hearing and deaf individuals.

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The efficiency of dynamic and static facial expression recognition

Cited by 39 publications

References 27 publications

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database)

Processing of Facial Emotion in Bipolar Depression and Euthymia

Quantifying Facial Expression Intensity and Signal Use in Deaf Signers

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