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90 results for “Facial expressions”
I see how you feel: facial expressions' recognition and distancing in the time of COVID-19
<p>Data sets and R script for anlysis.</p>
Perceiving arousal and valence in facial expressions: Differences between children and adults
<p>Arousal and valence have long been studied as the two primary dimensions for<br> the perception of emotional stimuli such as facial expressions. Prior correlational<br> studies that tested emotion perception along these dimensions found broad<br> similarities between adults and children. However, few studies looked for<br> direct differences between children and adults in these dimensions beyond<br> correlation. We tested 9-year-old children and adults on rating positive and<br> negative facial stimuli based on emotional arousal and valence. Despite high<br> significant correlations between children’s and adults’ ratings, our findings also<br> showed significant differences between children and adults in terms of rating<br> values: Children rated all expressions as significantly more positive than adults<br> in valence. Children also rated positive emotions as more arousing than adults.<br> Our results show that although perception of facial emotions along arousal and<br> valence follows similar patterns in children and adults, some differences in ratings<br> persist, and vary by emotion type.</p>
Developmental Changes in the Categorical Processing of Positive and Negative Facial Expressions
<p>Categorical biases in the processing of emotional facial expression have been the subject of much debate in the literature. Opposing views on this topic claim either that positive or negative facial expressions enjoy improved processing in the human brain. The developmental changes in the processing advantages of positive and negative facial expressions are also disputed, with studies using varying paradigms showing seemingly contradictory results. Therefore, to further investigate the development of categorical processing and extraction of emotional information from faces, we tested 6-, 9-, and 12-year-old children, as well as adults, on their ability to categorize various facial expressions as positive or negative as quickly as possible. This was a simplified paradigm designed to explicitly contrast the processing efficiency of positive and negative facial expressions on the broader level of those emotional valence categories, rather than specific single emotional expressions. Our results show an early age processing advantage for positive facial expressions, which disappears in adults who show no such differences in the case of response time measures. In the case of accuracy measures, the early advantage for positive facial expressions gradually disappears, and is reversed into a negativity advantage in adults. These findings demonstrate that category-based positive and negative processing advantages are strongly modulated by age over the course of development, and can exhibit opposite effects depending on the developmental stage of the participant. Here we archive the accuracy and and response time data used for our publication.</p>
The Significance of Emotional Facial Expression in Understanding Tears: Threat, Sincerity, and Cluster A Personality
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AUTH - Multimodal facial expressions dataset
<p>This is a dataset suitable for active and static single modality and multimodal facial expression recognition methods as well as other active perception tasks.. It contains (i) sequences of facial 3D models, (ii) Webots simulation environments and (ii) videos, along with synchronized audio (speech), captured from a grid of virtual cameras at 2 lighting levels. Each part of the dataset can be utilized in a standalone manner, i.e., it can be downloaded and utilized separately from the rest.</p>
Humanoid facial expressions as a tool to study human behaviour
<p>Dataset and MATLAB codes relative to the kinematic study titled <em>Humanoid facial expressions as a tool to study human behaviour (Lombardi G., Sciutti A., Rea F., Vannucci F., Di Cesare G, Scientific Reports 2024, DOI: https://doi.org/10.1038/s41598-023-45825-6) </em></p> <p><strong>analysis.m</strong> is the main matlab code for data organization and peak values extraction</p> <p><strong>VelocityCurves.m</strong>, <strong>AccelerationCurves.m</strong> and <strong>HeightCurves.m</strong> are codes to extract and plot mean velocity, acceleration and trajectory curves respectively and visualize comparison between congruent and incongruent conditions.</p> <p><strong>order.mat</strong> contains the order of experimental conditions</p> <p><strong>subject_kinamatic_data</strong> contains the kinematic dataset. Each subject folder contains different runs saved in .csv and .excel format (these are loaded in code analysis.m) </p> <p><strong>For additional information contact Dr. Alessandra Sciutti, alessandra.sciutti@iit.it</strong></p> <p> </p>
A fMRI dataset in response to large number of short natural dynamic facial expression videos
<p><span>Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although </span>the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a <span>small number of</span> lab-controlled video stimuli, how the human brain processes <span>natural</span> facial expressions still needs to be investigated. <span>T</span>o our knowledge<span>,</span> this type of data <span>specifically</span> <span>on </span><span>large number of </span><span>natural</span> <span>facial expression videos</span> is currently missing. <span>W</span>e describe <span>here </span>the <span>natural </span>Facial Expressions Dataset (NFED), a fMRI dataset <span>including </span>responses to 1,320 short (<span>3-second</span>) <span>natural</span> facial expression video clips. <span>These video clips is </span><span>annotated</span> <span>with three types of labels: emotion, gender, and ethnicity</span><span>, along with accompanying metadata</span>. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of <span>natural </span> <span>facial expression videos.</span></p>
A fMRI dataset in response to large-scale short natural dynamic facial expression videos
<p>Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural facial expressions still needs to be investigated. To our knowledge, this type of data specifically on large<span>-scale</span> natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), a fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips is annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.</p>
A comprehensive dataset of Infant Facial expressions of Pain Intensity
<p><span>Facial expression plays an import role in daily life routine whether people dealing each other or diagnosing internal feelings of one another. Facial expression of pain is very much import to recognize and properly handle. There is a large number of database representing facial expression of pain, including adult’s pain databases and infant’s pain databases. Most of the databases are focusing on adults pain expressions while some deals in infant’s pain expressions. This paper represent a new infants’ pains’ facial expression database called “Infant Facial Expression of Pain Intensity” (IFEPI). This database mainly focuses on four-pain levels i.e. No pain, Mild Pain, Moderate Pain and Severe Pain. The database consist of large collection of 25000 spontaneous images collected from 120 infants of two different hospitals of Khyber Pakhtunkhwa, Pakistan, works under Health department of Pakistan Government and UNICEF. The paper represent the process from setting environment to images/frame collections to the preprocessing and refinement of the images. This database is the largest database available on pain, also there is no database, available in our knowledge, of infant which deals with four level of pain intensity. To check the validation of the proposed database we use 4 state of the art machine learning models including logistic regression, SVM, KNN and Random Forest and 2 state of the art deep learning models including LeNet and AlexNet. As the database is the largest database available, the deep learning models i.e LeNet achieves training accuracy and testing accuracy of 98.2% and 86.3% respectively while AlexNet secure training and testing accuracy of 99.1% and 89.1% respectively</span></p>
Spontaneous mimicry of live facial expressions: A biological mechanism for emotional contagion
<p>Observation of live facial expressions typically elicits similar expressions (facial mimicry) accompanied by shared emotional experiences (emotional contagion). The model of embodied emotion proposes that emotional contagion and facial mimicry are functionally linked although the neural underpinnings are unknown. To address this knowledge gap we employed two-person (n = 20 dyads) functional near-infrared spectroscopy during live emotive face-processing while also measuring eye-tracking, facial classifications, and ratings of emotion. One partner, "Movie Watcher", was instructed to emote natural facial expressions while viewing evocative short movie clips. The other partner, "Face Watcher", viewed the Movie Watcher's face. Dyadic roles were alternated between partners. Task and rest blocks were implemented by timed epochs of clear and opaque glass that separated partners. Correlations of dyadic facial expressions (r = 0.41) and dyadic affect ratings (r = 0.66) were consistent with findings of both emotional contagion and facial mimicry. Neural correlates of emotional contagion based on covariates of partner ratings included right angular and supramarginal gyri. Neural correlates of mimicry associated with partner facial action units include core face recognition system. Thus, the proposed linkages between facial mimicry and emotional contagion represent separate components of face processing.</p>
Efficiency of Facial Expression Diagnostic System in Pain Assessment After Geriatric Surgery
ClinicalTrials.gov study NCT05756140. IPD Sharing: NO. Countries: 1. Publications: 1.
Efficacy and Safety of a New Botulinum Toxin Type A for Treatment of Facial Expression Lines
ClinicalTrials.gov study NCT01180348. IPD Sharing: YES. Countries: 1. Publications: 19.
Analysis of Facial Expressions for Pain Recognition in Fibromyalgia: Using Artificial Intelligence and Biomarkers
ClinicalTrials.gov study NCT06813352. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Comparison of Motor Imagery Technique and Mime Therapy on Facial Expressions in Facial Palsy Patients
ClinicalTrials.gov study NCT04993417. IPD Sharing: NO. Countries: 1. Publications: 9.
Evaluating Brain Responses to Facial Expressions in Major Depressive Disorder
ClinicalTrials.gov study NCT00071123. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Artificial Intelligence to Evaluate Postoperative Pain Based on Facial Expression
ClinicalTrials.gov study NCT05477303. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Recording Facial Expressions to Assess Taste in Children and Teenagers
ClinicalTrials.gov study NCT02923908. IPD Sharing: NO. Countries: 1. Publications: 8.
Data from: coyotes can do ‘puppy dog eyes’ too: comparing interspecific variation in Canis facial expression muscles
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Spontaneous mimicry of live facial expressions: A biological mechanism for emotional contagion
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The expression of care: alloparental care frequency predicts neural control of facial muscles in primates
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.