Introducing the Geneva Multimodal expression corpus for experimental research on emotion perception.

Bänziger, Tanja; Mortillaro, Marcello; Scherer, Klaus R.

doi:10.1037/a0025827

Cited by 343 publications

(388 citation statements)

References 57 publications

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“…The material used for behavioral analysis and ratings is part of a larger corpus of enacted EEs, the GEneva Multimodal Emotional Portrayal (GEMEP) corpus recorded at the University of Geneva (Bänziger, Mortillaro, & Scherer, 2011;Bänziger & Scherer, 2007. For the purpose of this study, we selected, from the 18 emotions represented in the corpus, four emotions that are believed to be communicated via reliable AUs: hot anger, panic fear, elation, and sadness.…”

Section: Study 2: Materialsmentioning

confidence: 99%

“…The inclusion of pride is also justified by the fact that three of the other four emotions are negative, implying that a possible difference between joy and the other emotions could simply reflect a difference between positive and negative valence instead of the effect of a specific emotion. Each emotion category includes expressions by 10 professional actors (five women), resulting in a total of 50 EEs (for further details about the GEMEP corpus, see Bänziger et al, 2011;Bänziger & Scherer, 2007.…”

Section: Study 2: Materialsmentioning

confidence: 99%

“…The video-only versions of the GEMEP EEs 4 (video clips with the sound removed, see Bänziger et al, 2011; were rated by 44 participants (21 women and 23 men, M age ϭ 22.2 years). Owing to technical problems during the rating study, five participants (one woman) had to be removed from the analyses, leaving a total of 39 participants.…”

Section: Ratingsmentioning

confidence: 99%

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Reliable facial muscle activation enhances recognizability and credibility of emotional expression.

Méhu

Mortillaro

Bänziger

et al. 2012

Emotion

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We tested Ekman's (2003) suggestion that movements of a small number of reliable facial muscles are particularly trustworthy cues to experienced emotion because they tend to be difficult to produce voluntarily. On the basis of theoretical predictions, we identified two subsets of facial action units (AUs): reliable AUs and versatile AUs. A survey on the controllability of facial AUs confirmed that reliable AUs indeed seem more difficult to control than versatile AUs, although the distinction between the two sets of AUs should be understood as a difference in degree of controllability rather than a discrete categorization. Professional actors enacted a series of emotional states using method acting techniques, and their facial expressions were rated by independent judges. The effect of the two subsets of AUs (reliable AUs and versatile AUs) on identification of the emotion conveyed, its perceived authenticity, and perceived intensity was investigated. Activation of the reliable AUs had a stronger effect than that of versatile AUs on the identification, perceived authenticity, and perceived intensity of the emotion expressed. We found little evidence, however, for specific links between individual AUs and particular emotion categories. We conclude that reliable AUs may indeed convey trustworthy information about emotional processes but that most of these AUs are likely to be shared by several emotions rather than providing information about specific emotions. This study also suggests that the issue of reliable facial muscles may generalize beyond the Duchenne smile.

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Section: Study 2: Materialsmentioning

confidence: 99%

Section: Study 2: Materialsmentioning

confidence: 99%

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Reliable facial muscle activation enhances recognizability and credibility of emotional expression.

Méhu

Mortillaro

Bänziger

et al. 2012

Emotion

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“…The emotion stimuli were taken from the GEneva Multimodal Emotion Portrayals (GEMEP) Corpus [7]. Our experiment contained three emotions (joy, anger and fear), which were most adopted in the relevant fMRI studies on emotion perception [5,8,9,10].…”

Section: Experiments and Data Collection Experimental Proceduresmentioning

confidence: 99%

Emotion Representation in Whole-brain Functional Connectivity Patterns: An fMRI Study

Liang¹

2018

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Abstract. The representation of emotion in human brain is an important question in the cognitive neuroscience. However, it was remained unclear to what extent the connections between different brain regions contribute to the emotion recognition. The present study mainly focused on the emotion decoding based on the functional connectivity patterns. We designed the experiment and collected the neural activities while participants viewed emotion stimuli using the functional magnetic resonance imaging (fMRI) technology. We constructed the whole-brain functional connectivity patterns for each emotion, and performed emotion classification using multivariate pattern analysis combined with machine learning algorithms. We found that emotions could be successfully decoded from the whole-brain functional connectivity patterns. These results provide new evidence that large-scale functional connectivity patterns contain rich emotion information and contribute to the emotion recognition. Our study extends exist fMRI studies on emotion perception and may further our understanding of how human beings achieve easy and quick recognition of emotions.

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“…Most facial databases were designed for the study of face recognition (Gross, 2005;www. face-rec.org/databases) and/or facial expression recognition, such as the Pictures of Facial Affect (Ekman & Friesen, 1976), the Karolinska Directed Emotional Faces (Lundqvist, Flykt, & Öhman, 1998), the PAL (Minear & Park, 2004), the Yale Face Database (Belhumeur, Hespanha, & Kriegman, 1997), the PICS (University of Stirling, http://pics.psych.stir.ac.uk), and the FACES (Max Planck Institute for Human Development in Berlin, http://faces.mpib-berlin.mpg.de). Several multimodal databases including faces and voices have also been developed for emotion research (Bänziger, Mortillaro, & Scherer, 2012;Petta et al, 2011).…”

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confidence: 99%

The Geneva Faces and Voices (GEFAV) database

et al. 2014

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Although many facial and vocal databases are available for research, very few of them have controlled the range of attractiveness of the stimuli that they offer. To fill this gap, we created the GEneva Faces and Voices (GEFAV) database, providing standardized faces (static and dynamic neutral, smiling) and voices (speaking sentences, vowels) of young European adults. A total of 61 women and 50 men 18-35 years old agreed to be part of the GEFAV stimuli, and two rating studies involving 285 participants provided evaluations of the facial and vocal samples. The final set of stimuli was satisfactory in terms of attractiveness range (wide and rather symmetrical distribution over the attractiveness continuum) and the reliability of the ratings (high consistency between the two rating studies, high interrater agreement in the final rating study). Moreover, the database showed an adequate validity, since a series of findings described by earlier research on human attractiveness were confirmed-namely, that facial and vocal attractiveness are predicted by femininity and health in women, and by masculinity, dominance, and trustworthiness in men. In future studies, the GEFAV stimuli may be used intact or transformed, individually or in multimodal combinations, to investigate a wide range of mechanisms, such as the behavioral, neuropsychological, and neurophysiological processes involved in social cognition.

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Introducing the Geneva Multimodal expression corpus for experimental research on emotion perception.

Cited by 343 publications

References 57 publications

Reliable facial muscle activation enhances recognizability and credibility of emotional expression.

Reliable facial muscle activation enhances recognizability and credibility of emotional expression.

Emotion Representation in Whole-brain Functional Connectivity Patterns: An fMRI Study

The Geneva Faces and Voices (GEFAV) database

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