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23 results for “Facial images”
Images associated to the paper "Evaluating the Sensitivity to Virtual Characters Facial Asymmetry in Emotion Synthesis"
<p>We conducted an experiment by presenting 64 pairs of static facial expressions, one symmetric and one asymmetric, illustrating eight emotions (three basic and five complex ones) alternatively for a male and a female character.<br> Each emotion was presented four times by swapping the symmetric and asymmetric positions and by mirroring the asymmetrical expression. Participants were asked to grade, on a continuous scale, the correctness of each facial expression with respect to a short definition</p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>Face orientation recognition is an important topic in computer vision and pattern recognition. Due to the non-rigid properties of faces, it is computationally expensive and difficult to achieve good recognition accuracy and robustness in face orientation recognition. In this paper, we propose an image mapping technique for face analysis in smart camera networks with a feature extraction and data from the facial feature. We estimate the face orientation angles in all camera views, based on the matched imaged data. Our objective is to obtain a set of facial structures which can work as landmarks for tracking and recognition of facial expressions. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features.</p>
Data from: facial growth and development trajectories based on 3D images: geometric morphometrics with a deformation perspective
<p>Developmental changes of facial shape are commonly investigated through geometric morphometrics. A limitation with this approach is the inability to investigate patterns of morphological changes at local scale. This could be addressed through quantifying the deformation required to deform one shape to another. This study aimed to investigate changes in mean, rate, and variance of facial shape at local scale using geometric morphometrics through deformation perspective. 2112 Europeans 3 to 40 years-old from the 3D Facial Norms project were included. Shape and rate trajectories from partial least-squares regressions revealed that the developmentally protrusive nasal bridge was due to local expansion in surrounding tissues as opposed to shape changes in nasal bridge per-ser. Local expansion of the supraorbital region, in particular the medial part in males, resulted in the sloping forehead and deep-situated eyes with development. Facial shape variation increased non-linearly with age (p < 0.05), with features having larger rate of change becoming more developmentally diversified. In summary, our deformation perspective facilitates unravelling morphogenetic processes underlying shape changes. Our extended analytical scope inspires novel measures worthy of consideration while establishing facial growth charts. The analytical framework in this study is broadly applicable for analysis of shape changes in general.</p>
The influence of social presence on facial affective responses to food images (FSC)
<p>Raw data from a study of social context and food liking. CSV files are generated by PsychoPy. ACQ files are generated by BIOPAC Acqknowledge software.</p> <p>Modified versions of Acqknowledge files have had markers for chocolate consumption readjusted by the researcher. These participants had failed to click the mouse in time with the start and finish of their eating episode. 188modified.acq file had the marker channel for the first trial edited because the fixation data for the first trial was missing.</p> <p> </p>
Data from: facial growth and development trajectories based on 3D images: geometric morphometrics with a deformation perspective
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Image Databases for Facial Analysis Coded for Race and Gender Features
<p> </p> <p>This document consists of the corpus of image databases examined for race and gender information as published in:</p> <p><em>Morgan Klaus Scheuerman, Kandrea Wade, Caitlin Lustig, and Jed R. Brubaker. 2020. How We’ve Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis. Proc. ACM Hum.-Comput. CSCW. </em></p> <p>This code book includes:</p> <p>1. Whether race/gender is present implicitly (as descriptive, but not annotated) or explicitly (annotated/labeled). </p> <p>2. What categories or descriptions of race/gender are used.</p> <p>3. Whether those categories/descriptions use underlying source material to justify or motivate their descriptions of race/gender.</p> <p>4. Whether explicitly annotated databases describe the process of annotating race/gender.</p>
the Effect of Viewing Idealized Smile Images Versus Nature Images Via Social Media on Immediate Facial Satisfaction in Young Adults: a Randomized Controlled Trial.
ClinicalTrials.gov study NCT05798650. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Stereo Photogrammetry Imaging in Normal Volunteers and Patients With Head and Facial Malformations
ClinicalTrials.gov study NCT00100529. IPD Sharing: Not stated. Countries: 3. Publications: 3.
3D Modeling of the Cervico-facial Region and Cranial Nerve Tractography: IMAG 2 ORL Project
ClinicalTrials.gov study NCT05763615. IPD Sharing: NO. Countries: 1. Publications: 5.
3-D Imaging Assessment of Scar Formation and Would Healing in Fat Grafted vs Non-Fat Grafted Facial Reconstruction Wound Sites
ClinicalTrials.gov study NCT01750424. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Intra-parotid Facial Nerve Imaging in Parotidectomy
ClinicalTrials.gov study NCT03822728. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: BMI and WHR are reflected in female facial shape and texture: a geometric morphometric image analysis
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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>
Data from: Perceptual expertise in forensic facial image comparison
Open the record for dataset details and reuse information.
Imaging of Facial Neuritis
ClinicalTrials.gov study NCT03543384. IPD Sharing: NO. 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.