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

Mapping Intimacies ◽

10.31234/osf.io/t7dg3 ◽

2021 ◽

Author(s):

Alessio Miolla ◽

Matteo Cardaioli ◽

Cristina Scarpazza

Keyword(s):

Standard Deviation ◽

Facial Expressions ◽

Emotional States ◽

Human Beings ◽

Emotional Facial Expressions ◽

Temporal Dynamic ◽

Intensity Perception ◽

The Mean ◽

Static Pictures ◽

Unique Dataset

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' interpretationof and reaction to various emotions. However, the scientific research on emotion mainly relied on static pictures of facial expressions posed (i.e., simulated) by actors, creating a signi?ficant bias in emotion literature. This dataset tries to ?ll 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. Furthermore, pictures displaying frame by frame the temporal dynamic of the expression, are also available for each clip. Notably, all stimuli were validated by 122 human observers. Hit rates for emotion and genuineness, as well as the mean, standard deviation of genuineness, and intensity perception, are provided for each clip.

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Perceiving Positive Facial Expression Can Relieve Depressive Moods: The Effect of Emotional Contagion on Mood in People With Subthreshold Depression

Frontiers in Psychology ◽

10.3389/fpsyg.2021.535980 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yuko Yamashita ◽

Tetsuya Yamamoto

Keyword(s):

Facial Expressions ◽

Emotional Contagion ◽

Subthreshold Depression ◽

Emotional States ◽

Emotional Facial Expressions ◽

Emotional Faces ◽

Before And After ◽

Mood Score ◽

Happy Mood ◽

Sad Mood

Emotional contagion is a phenomenon by which an individual’s emotions directly trigger similar emotions in others. We explored the possibility that perceiving others’ emotional facial expressions affect mood in people with subthreshold depression (sD). Around 49 participants were divided into the following four groups: participants with no depression (ND) presented with happy faces; ND participants presented with sad faces; sD participants presented with happy faces; and sD participants presented with sad faces. Participants were asked to answer an inventory about their emotional states before and after viewing the emotional faces to investigate the influence of emotional contagion on their mood. Regardless of depressive tendency, the groups presented with happy faces exhibited a slight increase in the happy mood score and a decrease in the sad mood score. The groups presented with sad faces exhibited an increased sad mood score and a decreased happy mood score. These results demonstrate that emotional contagion affects the mood in people with sD, as well as in individuals with ND. These results indicate that emotional contagion could relieve depressive moods in people with sD. It demonstrates the importance of the emotional facial expressions of those around people with sD such as family and friends from the viewpoint of emotional contagion.

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Not All Emotions Are Equal: Fear Chemosignals Lower Awareness Thresholds Only for Fearful Faces

Chemical Senses ◽

10.1093/chemse/bjaa047 ◽

2020 ◽

Vol 45 (7) ◽

pp. 601-608

Author(s):

Fábio Silva ◽

Nuno Gomes ◽

Sebastian Korb ◽

Gün R Semin

Keyword(s):

Facial Expressions ◽

Visual Processing ◽

Visual Awareness ◽

Emotional States ◽

Body Odor ◽

Emotional Facial Expressions ◽

Continuous Flash Suppression ◽

Fearful Faces ◽

Processing Stream ◽

Body Odors

Abstract Exposure to body odors (chemosignals) collected under different emotional states (i.e., emotional chemosignals) can modulate our visual system, biasing visual perception. Recent research has suggested that exposure to fear body odors, results in a generalized faster access to visual awareness of different emotional facial expressions (i.e., fear, happy, and neutral). In the present study, we aimed at replicating and extending these findings by exploring if these effects are limited to fear odor, by introducing a second negative body odor—that is, disgust. We compared the time that 3 different emotional facial expressions (i.e., fear, disgust, and neutral) took to reach visual awareness, during a breaking continuous flash suppression paradigm, across 3 body odor conditions (i.e., fear, disgust, and neutral). We found that fear body odors do not trigger an overall faster access to visual awareness, but instead sped-up access to awareness specifically for facial expressions of fear. Disgust odor, on the other hand, had no effects on awareness thresholds of facial expressions. These findings contrast with prior results, suggesting that the potential of fear body odors to induce visual processing adjustments is specific to fear cues. Furthermore, our results support a unique ability of fear body odors in inducing such visual processing changes, compared with other negative emotional chemosignals (i.e., disgust). These conclusions raise interesting questions as to how fear odor might interact with the visual processing stream, whilst simultaneously giving rise to future avenues of research.

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Cinematographic high-contrast lighting can facilitate empathic affective mimicry

10.31234/osf.io/3vqsj ◽

2019 ◽

Author(s):

Alan Voodla ◽

Elen Lotman ◽

Richard Naar ◽

Martin Kolnes ◽

Andero Uusberg

Keyword(s):

Facial Expressions ◽

Positive Emotions ◽

Affective Empathy ◽

Emotional States ◽

High Contrast ◽

Emotional Facial Expressions ◽

Low Contrast ◽

Facial Mimicry ◽

Facial Muscles ◽

Amplifying Effect

Can cinematographic lightning techniques be used to affect film viewers’ empathic reactions? We investigated the effect of high- and low-contrast lightning on affective empathy towards depicted actors. Fourty one participants watched short clips of professional actors expressing happiness, anger and disgust and rated the valence and intensity of their own and actors’ emotional states. The extent of facial mimicry of actors’ emotional facial expressions, quantified via electromyographic activation of expression-specific facial muscles, was used to assess affective empathy. Results indicated that high-contrast lighting had an amplifying effect on empathic mimicry of happy but not on angry expressions. The results suggest that high-contrast lightning could be used as a way to influence film viewer’s empathic reactions for positive emotions.

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Relationship of Depression and Organizational Conflicts with Job Performance in Professors and Staff of Larestan University of Medical Sciences

Occupational Hygiene and Health Promotion ◽

10.18502/ohhp.v5i1.6206 ◽

2021 ◽

Author(s):

Bahram KOUHNAVARD ◽

Fatemeh MAHMOUDIPOUR ◽

Fariba SAEEDI ◽

Negar TAMADONI JAHROMI ◽

Marzieh HONARBAKSH

Keyword(s):

Standard Deviation ◽

Job Performance ◽

Severe Depression ◽

Medical Personnel ◽

Organizational Conflict ◽

Human Beings ◽

Medical Sciences ◽

Cross Sectional ◽

The Mean ◽

Organizational Conflicts

Introduction: Considering the importance of maintaining the energy and health of the staff and faculty members as human beings, organizational conflict can have a significant impact on public health, performance, and the rate of depression in these individuals. The aim of this study was to investigate the relationship between job performance and depression with organizational conflicts in professors and staff of Larestan University of Medical Sciences. Methods: This descriptive-analytical cross-sectional study was conducted in 2016 on 82 professors and staff of Larestan University of Medical Sciences selected by census sampling method. In order to collect information in the present study, three questionnaires (organizational conflict, job performance, and Goldberg depression) were used. Results: The mean of depression was 58.20 with a standard deviation of 17.16, the mean of job performance was 30.55 with a standard deviation of 27.5, and the mean of organizational conflict was 93.20 with a standard deviation of 42.5. Conflict with subordinate had a higher mean score and conflict with peers had a lower mean score. The frequency distribution of depression among the participants showed that healthy individuals had the highest frequency (27.7%) and individuals with severe depression had the lowest frequency (4.8%). Conclusion: The findings showed an inverse relationship between depression and job performance, but a direct relationship was observed between depression and organizational conflict. By identifying and modifying the causes of stress in the workplace, we can see the improvement of performance and the creation of desirable and efficient feelings in medical personnel

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A Model for Generating Facial Expressions Using Virtual Emotion Based on Simple Recurrent Network

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2010.p0453 ◽

2010 ◽

Vol 14 (5) ◽

pp. 453-463 ◽

Cited By ~ 15

Author(s):

Yuki Matsui ◽

◽

Masayoshi Kanoh ◽

Shohei Kato ◽

Tsuyoshi Nakamura ◽

...

Keyword(s):

Facial Expression ◽

Dynamic System ◽

Facial Expressions ◽

Recurrent Network ◽

State Transitions ◽

Human Beings ◽

Emotional Facial Expressions ◽

Proposed Model ◽

Simple Recurrent Network ◽

Internal States

We propose an interactive facial expression model using the Simple Recurrent Network (SRN) for achieving interactions through facial expressions between robots and human beings. The proposed model counts humans in the root system as receivers of facial expressions to achieve a dynamic system bi-directionally affecting humans and robots. Robots typically generate only static changes in facial expression using motion files, so they seem bored, unnatural, and strange to their users. We use interactions between robots and people to diversity the inputs of robots and use emotional state transitions of robots to reduce uniformities in output facial expressions. This paper discusses a dynamic system that causes the proposed model to learn emotional facial expressions based on those of humans. Next, we regard internal states generated by the proposed model as virtual emotions and show that mixed emotions can be expressed by users’ inputs from the virtual emotional space. Moreover, based on the results of a questionnaire, we see that facial expressions adopted in the virtual emotional space of the proposed model received high rates of approval from the users.

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