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90 results for “Facial expression”
Effect of verbal interference and response hand on hemisphere asymmetries in sad facial expression processing
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TMS over the posterior cerebellum modulates motor cortical excitability in response to facial emotional expressions
<p>Evidence suggests that the posterior cerebellum is involved in emotional processing. Specific mechanisms by which the cerebellum contributes to the perception of and reaction to the emotional state of others are not well-known. It is likely that perceived emotions trigger anticipatory/preparatory motor changes. However, the extent to which the cerebellum modulates the activity of the motor cortex to contribute to emotional processing has not been directly investigated. In this study, we assessed whether the activity of the posterior cerebellum influences the modulation of motor cortical excitability in response to emotional stimuli. To this end, we transiently disrupted the neural activity of the left posterior cerebellum using 1 Hz repetitive transcranial magnetic stimulation (rTMS) and examined its effect on motor cortical excitability witnessed during emotional face processing (in comparison to the effects of sham rTMS). Motor excitability was measured as TMS-based motor evoked potentials (MEPs) recorded from bilateral first dorsal interosseous (FDI) muscles during the viewing of negative emotional (i.e. fearful) and neutral facial expressions. In line with previous evidence, we found that MEP amplitude was increased during the viewing of fearful compared to neutral faces. Critically, when left posterior cerebellar activity was transiently inhibited with 1 Hz rTMS, we observed a reduction in amplitude of MEPs recorded from the contralateral (right) motor cortex during the viewing of emotional (but not neutral) faces. In turn, inhibition of the left posterior cerebellum did not affect the amplitude of MEPs recorded from the ipsilateral motor cortex. Our findings suggest that the posterolateral (left) cerebellum modulates motor cortical response to negative emotional stimuli and may serve as an interface between limbic, cognitive, and motor systems.</p>
Can an android's posture and movement discriminate against the ambiguous emotion perceived from its facial expressions?
<p>Android's face and body expressions for experiment stimuli.</p>
Data from: The influence of a working memory task on affective perception of facial expressions
In a dual-task paradigm, participants performed a spatial location working memory task and a forced two-choice perceptual decision task (neutral vs. fearful) with gradually morphed emotional faces (neutral ~ fearful). Task-irrelevant word distractors (negative, neutral, and control) were experimentally manipulated during spatial working memory encoding. We hypothesized that, if affective perception is influenced by concurrent cognitive load using a working memory task, task-irrelevant emotional distractors would bias subsequent perceptual decision-making on ambiguous facial expression. We found that when either neutral or negative emotional words were presented as task-irrelevant working-memory distractors, participants more frequently reported fearful face perception - but only at the higher emotional intensity levels of morphed faces. Also, the affective perception bias due to negative emotional distractors correlated with a decrease in working memory performance. Taken together, our findings suggest that concurrent working memory load by task-irrelevant distractors has an impact on affective perception of facial expressions.
GAN Generated Images for Facial Expression Recognition systems
<p>Most facial expression recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques has been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems. To advance in this respect, we evaluate the impact of the GAN techniques by creating a new DB containing the generated synthetic images. </p> <p>The face images contained in the KDEF DB serve as the basis for creating novel synthetic images by combining the facial features of two images (i.e., Candie Kung and Cristina Saralegui) selected from the YouTube-Faces DB. The novel images differ from each other, in particular concerning the eyes, the nose, and the mouth, whose characteristics are taken from the Candie and Cristina images.</p> <p>The total number of novel synthetic images generated with the GAN is 980 (70 individuals from KDEF DB x 7 emotions x 2 subjects from YouTube-Faces DB).</p> <p>The zip file "GAN_KDEF_Candie" contains the 490 images generated by combining the KDEF images with the Candie Kung image. The zip file "GAN_KDEF_Cristina" contains the 490 images generated by combining the KDEF images with the Cristina Saralegui image. The used image IDs are the same used for the KDEF DB. The synthetic generated images have a resolution of 562x762 pixels.</p> <p> </p> <p><strong>If you make use of this dataset, please consider citing the following publication:</strong></p> <p>Porcu, S., Floris, A., & Atzori, L. (2020). Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems. Electronics, 9, 1892, doi: 10.3390/electronics9111892, url: https://www.mdpi.com/2079-9292/9/11/1892.</p> <p>BibTex format:</p> <p>@article{porcu2020evaluation, title={Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems}, author={Porcu, Simone and Floris, Alessandro and Atzori, Luigi}, journal={Electronics}, volume={9}, pages={108781}, year={2020}, number = {11}, article-number = {1892}, publisher={MDPI}, doi={10.3390/electronics9111892} }</p> <p> </p>
The pain hidden in your hands: facial expression of pain reduces the influence of goal-related information in action recognition
<p>data and code associated to manuscript entitled "The pain hidden in your hands: facial expression of pain reduces the influence of goal-related information in action recognition"</p>
Brain Activities Associated With Different Facial Expressions Using Blood Oxygenation Level Dependent Functional MRI
ClinicalTrials.gov study NCT00096564. IPD Sharing: Not stated. Countries: 1. Publications: 3.
An Observer Rating Scale of Facial Expression Can Predict Dreaming in Propofol Anesthesia
ClinicalTrials.gov study NCT04235894. IPD Sharing: NO. Countries: 0. Publications: 19.
Data from: Functionally relevant responses to human facial expressions of emotion in the domestic horse (Equus caballus)
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Data from: Perceiving the evil eye: investigating hostile interpretation of ambiguous facial emotional expression in violent and non-violent offenders
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Data from: The influence of a working memory task on affective perception of facial expressions
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Non-coding variants alter Gata2 expression in rhombomere 4 motor neurons and cause dominant hereditary congenital facial paresis
GEO Series GSE223274. Mus musculus. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Effect of I-125 Seed Implantation on Gene Expression in the Facial Nerve of Rabbits with Facial Nerve Injury
GEO Series GSE312643. Oryctolagus cuniculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profiling of Jun -/- and Jun +/+ facial motor neurons
GEO Series GSE162977. Mus musculus. 37 samples. Type: Expression profiling by array.
[LS2N_IPI_DisFER] Comparing the Robustness of Humans and Deep Neural Networks on Facial Expression Recognition
<h2>DisFER</h2> <p>Distorted-FER (DisFER), a new facial expression recognition (FER) dataset composed of a wide number of distorted images of faces.</p> <p> </p> <h2>Materials and Methods</h2> <h4>Dataset</h4> <div>The source images used in our experiment come from the Facial Expression Recognition 2013 (FER-2013) dataset [1]. This dataset was firstly introduced in 2013 at the International Conference on Machine Learning, and has been used in a large number of research works since then, as it encompasses naturalistic conditions and challenges. This dataset consists of 35,887 images of faces in 48 × 48 format, collected thanks to a Google search. Human accuracy on FER-2013 was estimated by its authors around 65.5% [1].</div> <div>To build the Distorted-FER (DisFER) dataset, we randomly selected, from FER-2013, twelve images per basic emotion, as defined by Ekman [2] (i.e., anger, disgust, fear, happiness, neutral, sadness, and surprise). This yields a total of 84 source images. Each original stimulus was then distorted using three different types of distortions, i.e., Gaussian blur (GB), Gaussian noise (GN), and salt-and-pepper noise (SP). Each distortion was applied at distinct levels: three standard deviation values were tested for GB, i.e., 0.8, 1.1, and 1.4; similarly for GN with standard deviation values equal to 10, 20, and 30; while probability levels of 0.02, 0.04, and 0.06 were chosen for SP; corresponding to low, medium, and high distortions, respectively. </div> <div> </div> <div> <h4>Crowdsourcing Experiment</h4> <div>In order to collect as many votes as possible on our dataset, and because rating 840 images is time-consuming and can be extremely tiring for a single participant, we decided to set up a crowdsourcing experiment. Such experiments indeed allow the conduct of large-scale subjective tests with reduced costs and efforts.</div> <div>The DisFER dataset was therefore split into twenty-one playlists of forty images each, with a view to keep the tests as fast as possible—as crowdsourcing experiments should not last more than ten minutes or so. Playlists were carefully designed to contain the same numbers of images of a given configuration (i.e., emotion, distortion types, and distortion levels). Among a playlist, images were randomly displayed to participants.</div> <div>Each participant was asked to choose which emotion (i.e., anger, disgust, fear, happiness, neutral, sadness, or surprise) they recognized in the displayed image. No time constraint was imposed on participants to fulfill the task.</div> <div> </div> <div>A total of 1051 participants (including 50% of females) were recruited using the Prolific platform [3]. Prolific takes into consideration researchers’ needs by maintaining a subject recruitment process that is similar to that of a laboratory experiment. Indeed, participants are fully informed that they are being recruited for a research study. Consequently, this platform allows researchers to eliminate ethical concerns, and it further improves the reliability of collected data.</div> <div> </div> Participants were aged between 19 and 75 years old (with a mean of 30±8.53 -- note that three participants did not wish to respond). Twenty playlists out of twenty-one were entirely watched and rated by fifty distinct participants, whereas one playlist was watched and evaluated by fifty-one participants.</div> <div> </div> <div> <h2>References</h2> </div> <div>[1] Goodfellow, I.J.; Erhan, D.; Carrier, P.L.; Courville, A. Challenges in Representation Learning: A Report on Three Machine Learning Contests. In Proceedings of the Neural Information Processing, Daegu, South Korea, 3–7 November 2013; Springer: Berlin/Heidelberg, Germany, 2013; pp. 117–124</div> <div>[2] Ekman, P. An argument for basic emotions. Cogn. Emot. <strong>1992</strong>, 6, 169–200</div> <div>[3] https://www.prolific.com/</div> <div> </div>
A novel database of Teenager's spontaneous facial expressions
<p>This dataset presents a comprehensive collection of spontaneous facial expressions obtained from 14 teenagers, encompassing six fundamental emotions: Anger, Disgust, Fear, Happy, Sad, and Surprise. With a total of 314 videos, the dataset comprises approximately 59,000 emotional frames, including both emotional expressions and neutral states. Recorded using an HD Webcam with 1080P resolution and a frame rate of 30 fps, the dataset captures authentic and unposed facial expressions, distinguishing it from existing databases. Its diverse subjects from various backgrounds enable cross-cultural and ethnic studies of facial expressions and emotions. Addressing the scarcity of similar datasets focused on teenagers, the dataset underwent thorough validation by seven validators, achieving a 64% average accuracy in emotion recognition. The dataset's potential applications range from enhancing facial expression recognition systems to investigating the impact of facial and body movements, while also providing a valuable resource for benchmarking and comparison within the vision community.</p>
Facial Expression Recognition of Emotion and Categorization of Emotional Words in Gilles de la Tourette's Syndrome
ClinicalTrials.gov study NCT00664300. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Perception of Facial Expressions Following Correction of Wrinkles and Folds
ClinicalTrials.gov study NCT02573337. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Autonomic Modulation and Facial Expression in Autism Spectrum Disorder
ClinicalTrials.gov study NCT02997566. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Using Models' Facial Expressions to Enhance Healthy Eating Behaviour in Children
ClinicalTrials.gov study NCT04988854. IPD Sharing: YES. Countries: 1. Publications: 0.
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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.