scholarly journals Wearing N95, Surgical and Cloth Face Masks Compromises the Communication of Emotion

2020 ◽  
Author(s):  
Andrew Langbehn ◽  
Dasha Yermol ◽  
Fangyun Zhao ◽  
Christopher Thorstenson ◽  
Paula Niedenthal
Keyword(s):  
Facial Expression ◽  
Political Attitudes ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Target Signal ◽  
Facial Action ◽  
Target Emotion ◽  
The Face ◽  
Positive Feelings ◽  
Human Faces

Abstract According to the familiar axiom, the eyes are the window to the soul. However, wearing masks to prevent the spread of COVID-19 involves occluding a large portion of the face. Do the eyes carry all of the information we need to perceive each other’s emotions? We addressed this question in two studies. In the first, 162 Amazon Mechanical Turk (MTurk) workers saw videos of human faces displaying expressions of happiness, disgust, anger, and surprise that were fully visible or covered by N95, surgical, or cloth masks and rated the extent to which the expressions conveyed each of the four emotions. Across mask conditions, participants perceived significantly lower levels of the expressed (target) emotion and this was particularly true for expressions composed of greater facial action in the lower part of the faces. Furthermore, higher levels of other (non-target) emotions were perceived in masked compared to visible faces. In the second study, 60 MTurk workers rated the extent to which three types of smiles (reward, affiliation, and dominance smiles), either visible or masked, conveyed positive feelings, reassurance, and superiority. They reported that masked smiles communicated less of the target signal than visible faces, but not more of other possible signals. Political attitudes were not systematically associated with disruptions in the processing of facial expression caused by masking the face.

Designing Emotion Of Characters By Referencing From Facs In Short Animated Film “RANA”

2018 ◽  
Vol 9 (2) ◽  
pp. 31-38
Author(s):  
Fransisca Adis ◽  
Yohanes Merci Widiastomo
Keyword(s):  
Facial Expression ◽  
Basic Characteristic ◽  
Early Stage ◽  
3D Animation ◽  
Coding System ◽  
Facial Action Coding System ◽  
Facial Action ◽  
Animated Film ◽  
Character Design ◽  
The Face

Facial expression is one of some aspects that can deliver story and character’s emotion in 3D animation. To achieve that, we need to plan the character facial from very beginning of the production. At early stage, the character designer need to think about the expression after theu done the character design. Rigger need to create a flexible rigging to achieve the design. Animator can get the clear picture how they animate the facial. Facial Action Coding System (FACS) that originally developed by Carl-Herman Hjortsjo and adopted by Paul Ekman and Wallace V. can be used to identify emotion in a person generally. This paper is going to explain how the Writer use FACS to help designing the facial expression in 3D characters. FACS will be used to determine the basic characteristic of basic shapes of the face when show emotions, while compare with actual face reference. Keywords: animation, facial expression, non-dialog

Neural Responses to Facial Expression and Face Identity in the Monkey Amygdala

2007 ◽  
Vol 97 (2) ◽  
pp. 1671-1683 ◽  
Author(s):  
K. M. Gothard ◽  
F. P. Battaglia ◽  
C. A. Erickson ◽  
K. M. Spitler ◽  
D. G. Amaral
Keyword(s):  
Facial Expression ◽  
Firing Rate ◽  
Face Processing ◽  
Neural Responses ◽  
Firing Rates ◽  
Face Identity ◽  
Relative Contribution ◽  
The Face ◽  
Face Stimuli ◽  
Human Faces

The amygdala is purported to play an important role in face processing, yet the specificity of its activation to face stimuli and the relative contribution of identity and expression to its activation are unknown. In the current study, neural activity in the amygdala was recorded as monkeys passively viewed images of monkey faces, human faces, and objects on a computer monitor. Comparable proportions of neurons responded selectively to images from each category. Neural responses to monkey faces were further examined to determine whether face identity or facial expression drove the face-selective responses. The majority of these neurons (64%) responded both to identity and facial expression, suggesting that these parameters are processed jointly in the amygdala. Large fractions of neurons, however, showed pure identity-selective or expression-selective responses. Neurons were selective for a particular facial expression by either increasing or decreasing their firing rate compared with the firing rates elicited by the other expressions. Responses to appeasing faces were often marked by significant decreases of firing rates, whereas responses to threatening faces were strongly associated with increased firing rate. Thus global activation in the amygdala might be larger to threatening faces than to neutral or appeasing faces.

scholarly journals Facing disinformation: Five methods to counter conspiracy theories amid the Covid-19 pandemic

Comunicar ◽  
2021 ◽  
Vol 29 (69) ◽  
Author(s):  
Tianru Guan ◽  
Tianyang Liu ◽  
Randong Yuan
Keyword(s):  
Cognitive Processes ◽  
Mechanical Turk ◽  
Conspiracy Theory ◽  
Amazon Mechanical Turk ◽  
Conspiracy Theories ◽  
Urgent Task ◽  
Intergroup Relationships ◽  
The Face ◽  
Conspiracy Beliefs ◽  
Medios De Comunicacion

Among the burgeoning discussions on the argumentative styles of conspiracy theories and the related cognitive processes of their audiences, research thus far is limited in regard to developing methods and strategies that could effectively debunk conspiracy theories and reduce the harmful influences of conspiracist media exposure. The present study critically evaluates the effectiveness of five approaches to reducing conspiratorial belief, through experiments (N=607) conducted on Amazon Mechanical Turk. Our results demonstrate that the content-based methods of counter conspiracy theory can partly mitigate conspiratorial belief. Specifically, the science- and fact-focused corrections were able to effectively mitigate conspiracy beliefs, whereas media literacy and inoculation strategies did not produce significant change. More crucially, our findings illustrate that both audience-focused methods, which involve decoding the myth of conspiracy theory and re-imagining intergroup relationships, were effective in reducing the cognitive acceptance of conspiracy theory. Building on these insights, this study contributes to a systematic examination of different epistemic means to influence (or not) conspiracy beliefs -an urgent task in the face of the infodemic threat apparent both during and after the COVID-19 pandemic. Entre las crecientes discusiones sobre los estilos argumentativos de las teorías de conspiración y los procesos cognitivos relacionados de su público, los estudios hasta ahora son limitados en lo que respecta al desarrollo de métodos y estrategias que podrían desacreditar eficazmente las teorías de conspiración y reducir las influencias dañinas de la exposición a los medios de comunicación conspirativos. El presente estudio evalúa de manera crítica la efectividad de cinco enfoques para reducir la creencia en conspiraciones, a través de experimentos (N=607) realizados en Amazon Mechanical Turk. Nuestros resultados demuestran que los métodos basados en el contenido al enfrentar las teorías de la conspiración pueden mitigar parcialmente la creencia conspiratoria. Específicamente, las correcciones centradas en la ciencia y los hechos fueron capaces de mitigar eficazmente las creencias en la conspiración, mientras que las estrategias de alfabetización mediática e inoculación no produjeron cambios significativos. Más importante aún, nuestros hallazgos ilustran que ambos métodos centrados en el público, que implican decodificar el mito de la teoría de la conspiración y reimaginar las relaciones intergrupales, fueron efectivos para reducir la aceptación cognitiva de la teoría de la conspiración. Basado en estos conocimientos, este estudio contribuye a un examen sistemático de distintos medios epistemológicos para influir (o no) en las creencias conspirativas, una tarea urgente frente a la evidente amenaza infodémica, tanto durante como después de la pandemia de COVID-19.

scholarly journals Facial Expression Recognition using CNN

2020 ◽  
pp. 179-183
Author(s):  
S. Mary Hima Preethi ◽  
P. Sobha ◽  
P. Rajalakshmi Kamalini ◽  
K. Gowri Raghavendra Narayan
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Recognition ◽  
Confusion Matrix ◽  
Neural System ◽  
Expression Recognition ◽  
The Face ◽  
Human Faces ◽  
The Ideal

People have consistently been able to perceive and recognize faces and their feelings. Presently PCs can do likewise. We propose a model which recognizes human faces and classifies the emotion on the face as happy, angry, sad, neutral, surprise, disgust or fear. It is developed utilizing a convolutional neural network(CNN) and involves various stages. All these are carried out using a dataset available on the Kaggle repository named fer2013. Precision and execution of the neural system can be assessed utilizing a confusion matrix. We applied cross-approval to decide the ideal hyper-parameters and assessed the presentation of the created models by looking at their training histories.

scholarly journals GAMBARAN MUSCULI FACIALIS PADA EKSPRESI WAJAH DAN EMOSI DENGAN MENGGUNAKAN FACIAL ACTION CODING SYSTEM PADA CALON PRESIDEN JOKOWI

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Friska G. Batoteng ◽  
Taufiq F. Pasiak ◽  
Shane H. R. Ticoalu
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Expression Recognition ◽  
Coding System ◽  
Facial Action Coding System ◽  
Orbicularis Oculi ◽  
Facial Action ◽  
Facial Muscles ◽  
The Face ◽  
Facial Action Coding

Abstract: Facial expression recognition is one way to recognize emotions which has not received much attention. Muscles that form facial expressions known as musculli facial, muscles that move the face and form human facial expressions: happy, sad, angry, fearful, disgusted and surprised which are the six basic expressions of human emotion. Human facial expressions can be measured using FACS (Facial Action Coding System). This study aims to determine the facial muscles which most frequently used and most rarely used, and determine the emotion expression of Jokowi, a presidential candidate, through assessment of the facial muscles using FACS. This study is a retrospective descriptive study. The research samples are the whole photo of Jokowi’s facial expression at first presidential debate in 2014, about 30 photos. Samples were taken from a video debate and confirmed to be a photo using Jokowi’s facial expressions which then further analyzed using FACS. The research showed that the most used action units and facial muscle is AU 1 whose work on frontal muscle pars medialis (14.75%). The least appear muscles on Jokowi’s facial expressions were musculus orbicularis oculi, pars palpebralis and AU 24 musculus obicularis oris (0.82%). The dominant facial expressions was seen in Jokowi was sad facial expression (36.67%).Keywords: musculi facialis, facial expression, expression of emotion, FACSAbstrak: Pengenalan ekspresi wajah adalah salah satu cara untuk mengenali emosi yang belum banyak diperhatikan. Otot-otot yang membentuk ekspresi wajah yaitu musculli facialis yang merupakan otot-otot penggerak wajah dan membentuk ekspresi – ekspresi wajah manusia yaitu bahagia, sedih, marah, takut, jijik dan terkejut yang merupakan 6 dasar ekspresi emosi manusia. Ekspresi wajah manusia dapat diukur dengan menggunakan parameter FACS (Facial Action Coding System). Penelitian ini bertujuan untuk mengetahui musculi facialis yang paling sering digunakan dan yang paling jarang digunakan, serta untuk menentukan ekspresi emosi calon presiden Jokowi. Desain penelitian ini yaitu penelitian deskriptif dengan retrospektif. Sampel penelitian ialah seluruh foto ekspresi wajah Jokowi saat debat calon presiden pertama tahun 2014 sebanyak 30 foto. Sampel diambil dari video debat dan dikonfirmasi menjadi foto kemudian dianalisis lebih lanjut menggunakan FACS. Penelitian ini didapatkan hasil bahwa Musculi yang paling banyak digerakkan, yaitu Musculi frontalis pars medialis (14,75%). Musculi yang paling sedikit muncul pada ekspresi wajah Jokowi yaitu musculus orbicularis oculi, pars palpebralis dan musculus obicularis oris (0,82%). Ekspresi wajah yang dominan dinampakkan oleh Jokowi merupakan ekspresi wajah sedih (36,67%).Kata kunci: musculi facialis, ekspresi wajah, ekspresi emosi, FACS

Asymmetries of Facial Motility during the Dissimulation of Emotion

1996 ◽  
Vol 83 (1) ◽  
pp. 263-274 ◽  
Author(s):  
Andrea Clarici ◽  
Francesca Melon ◽  
Susanne Braun ◽  
Antonio Bava
Keyword(s):  
Facial Expression ◽  
Voluntary Control ◽  
Asymmetric Distribution ◽  
Coding System ◽  
Facial Action Coding System ◽  
Facial Mimicry ◽  
Facial Action ◽  
The Face ◽  
Experimental Subjects ◽  
Facial Action Coding

The asymmetries of facial expression were estimated in a sample of 14 experimental subjects with the Facial Action Coding System during voluntary control of facial mimicry while viewing videotapes. The subjects were instructed to express facially the emotion experienced or to dissimulate their true emotion with a facial expression opposite (incongruous) to what they actually felt. Only during dissimulation did facial mimicry show an asymmetric distribution toward the lower left side of the face.

scholarly journals Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3046
Author(s):  
Shervin Minaee ◽  
Mehdi Minaei ◽  
Amirali Abdolrashidi
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Visualization Technique ◽  
Convolutional Network ◽  
The Past ◽  
Multiple Datasets ◽  
The Face ◽  
Traditional Approaches

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

The face of future: Face expressions during future thinking

2021 ◽  
pp. 174702182199299
Author(s):  
Mohamad El Haj ◽  
Emin Altintas ◽  
Ahmed A Moustafa ◽  
Abdel Halim Boudoukha
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Emotional Experience ◽  
Future Scenarios ◽  
Future Thinking ◽  
Analysis Software ◽  
Emotional Facial Expressions ◽  
Facial Analysis ◽  
The Face ◽  
Face Expressions

Future thinking, which is the ability to project oneself forward in time to pre-experience an event, is intimately associated with emotions. We investigated whether emotional future thinking can activate emotional facial expressions. We invited 43 participants to imagine future scenarios, cued by the words “happy,” “sad,” and “city.” Future thinking was video recorded and analysed with a facial analysis software to classify whether facial expressions (i.e., happy, sad, angry, surprised, scared, disgusted, and neutral facial expression) of participants were neutral or emotional. Analysis demonstrated higher levels of happy facial expressions during future thinking cued by the word “happy” than “sad” or “city.” In contrast, higher levels of sad facial expressions were observed during future thinking cued by the word “sad” than “happy” or “city.” Higher levels of neutral facial expressions were observed during future thinking cued by the word “city” than “happy” or “sad.” In the three conditions, the neutral facial expressions were high compared with happy and sad facial expressions. Together, emotional future thinking, at least for future scenarios cued by “happy” and “sad,” seems to trigger the corresponding facial expression. Our study provides an original physiological window into the subjective emotional experience during future thinking.

scholarly journals Charged With a Crime: The Neuronal Signature of Processing Negatively Evaluated Faces Under Different Attentional Conditions

2021 ◽  
pp. 095679762199666
Author(s):  
Sebastian Schindler ◽  
Maximilian Bruchmann ◽  
Claudia Krasowski ◽  
Robert Moeck ◽  
Thomas Straube
Keyword(s):  
Event Related Potentials ◽  
Emotional Information ◽  
Early Posterior Negativity ◽  
Information Studies ◽  
The Face ◽  
Related Potentials ◽  
Feature Based ◽  
Human Faces ◽  
Emotional Knowledge ◽  
Late Stages

Our brains rapidly respond to human faces and can differentiate between many identities, retrieving rich semantic emotional-knowledge information. Studies provide a mixed picture of how such information affects event-related potentials (ERPs). We systematically examined the effect of feature-based attention on ERP modulations to briefly presented faces of individuals associated with a crime. The tasks required participants ( N = 40 adults) to discriminate the orientation of lines overlaid onto the face, the age of the face, or emotional information associated with the face. Negative faces amplified the N170 ERP component during all tasks, whereas the early posterior negativity (EPN) and late positive potential (LPP) components were increased only when the emotional information was attended to. These findings suggest that during early configural analyses (N170), evaluative information potentiates face processing regardless of feature-based attention. During intermediate, only partially resource-dependent, processing stages (EPN) and late stages of elaborate stimulus processing (LPP), attention to the acquired emotional information is necessary for amplified processing of negatively evaluated faces.

scholarly journals Evaluating extinction, renewal, and resurgence of operant behavior in humans with Amazon Mechanical Turk

2021 ◽  
Vol 74 ◽  
pp. 101728
Author(s):  
Carolyn M. Ritchey ◽  
Toshikazu Kuroda ◽  
Jillian M. Rung ◽  
Christopher A. Podlesnik
Keyword(s):  
Operant Behavior ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk
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