Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “face recognition”
Raw data for Facemasks and face recognition: Potential impact on synaptic plasticity
<p>Figure 2 legend Upper panel. In control condition, visual sensory inputs from in- dividual’s face are encoded by the face recognition system. At system level (a), this process implies functional and structural modifications in multiple brain regions, whereas at cellular level (b), this promotes the induction of distinct forms of synaptic plasticity, such as long-term potentiation and long-term depression (LTP, LTD, respectively). Lower panel. Wearing face masks consis- tently reduces the amount of information, by excluding the lower part of the face, including nose and mouth. Thus, both at system and cellular level, such mismatch impairs long-term functional and structural plasticity. In particular, at synaptic level, LTP induction will be favored, whereas LTD will be impaired. The black traces indicate the excitatory postsynaptic potentials in control condition; the red traces represent the long-term changes in synaptic efficacy after the induction protocol.</p>
Figure 3. 46 points are selected on face elements to describe the emotions.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>The number of points and the position of points are not standardized, but it is depending on<br> the features that will be extracted, and used for the classifier. Many researches use various number<br> of points and positions based on their view about the feature to be considered [13] [18] [19]. Figure<br> 3 shows the points we used.</p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)
<p>To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 1. Face area detection
<p>The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The result is shown in Figure 1. Since each portion of the image used to detect a feature is much smaller than that of the whole image, detection of all three facial features takes less time on average than detecting the face itself. Using a 1.2GHz AMD processor to analyze a 320 by 240 image, a frame rate of 3 frames per second was achieved. Since a frame rate of 5 frames per second was achieved in facial detection only by using a much faster processor, regionalization provides a tremendous increase in efficiency in facial feature detection. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 1. Face area detection
<p>The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The result is shown in Figure 1. Since each portion of the image used to detect a feature is much smaller than that of the whole image, detection of all three facial features takes less time on average than detecting the face itself. Using a 1.2GHz AMD processor to analyze a 320 by 240 image, a frame rate of 3 frames per second was achieved. Since a frame rate of 5 frames per second was achieved in facial detection only by using a much faster processor, regionalization provides a tremendous increase in efficiency in facial feature detection. </p>
Face mask detection and masked facial recognition dataset (MDMFR Dataset)
<p>The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (<a href="https://www.sciencedirect.com/science/article/pii/S1319157821003633#b0170">MDMFR, 2022</a>) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, <a href="https://www.sciencedirect.com/topics/computer-science/illumination-condition">illumination conditions</a>, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (<a href="https://www.sciencedirect.com/science/article/pii/S1319157821003633#b0215">Plastiliq, 2022</a>).</p>
Dataset from 'Billino, J., van Belle, G., Rossion, B., & Schwarzer, G. (2018). The nature of individual face recognition in preschool children: Insights from a gaze-contingent paradigm. Cognitive Development, 47, 168-180. DOI: 10.1016/j.cogdev.2018.06.007
<p>The folder contains a data file and a description file providing column labels.</p> <p>For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p>
Data from: Two areas for familiar face recognition in the primate brain
Familiarity alters face recognition: Familiar faces are recognized more accurately than unfamiliar ones and under difficult viewing conditions when unfamiliar face recognition fails. The neural basis for this fundamental difference remains unknown. Using whole-brain functional magnetic resonance imaging, we found that personally familiar faces engage the macaque face-processing network more than unfamiliar faces. Familiar faces also recruited two hitherto unknown face areas at anatomically conserved locations within the perirhinal cortex and the temporal pole. These two areas, but not the core face-processing network, responded to familiar faces emerging from a blur with a characteristic nonlinear surge, akin to the abruptness of familiar face recognition. In contrast, responses to unfamiliar faces and objects remained linear. Thus, two temporal lobe areas extend the core face-processing network into a familiar face-recognition system.
Training images for physical stress image face recognition
<p>A proof-of-concept dataset for the initial training for physical stress recognition based on patient's faces. The dataset consists on multiple frames of a training session containing 11 different individuals.</p>
Self-Face Recognition After Face Transplantation
ClinicalTrials.gov study NCT03027141. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Two areas for familiar face recognition in the primate brain
Open the record for dataset details and reuse information.
Face Perception vs. Memory Training to Improve Face Recognition in Developmental Prosopagnosia
ClinicalTrials.gov study NCT05800782. IPD Sharing: YES. Countries: 1. Publications: 0.
The effect of face masks and sunglasses on identity and expression recognition with super-recognisers and typical observers
Open the record for dataset details and reuse information.
Evaluating Face-Recognition Technology in Syndrome Diagnosis
ClinicalTrials.gov study NCT04709965. IPD Sharing: NO. Countries: 1. Publications: 0.
Computer-based Training of Face Recollection to Improve Face Recognition in Developmental Prosopagnosia
ClinicalTrials.gov study NCT04799340. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Face Anthropometric Pattern Recognition Technology for Computer Aided Diagnosis of Human Genetic Disorders.
ClinicalTrials.gov study NCT00705055. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multimodal Data-assisted Primary Screening for Allergic Rhinitis Based on Voice Recognition and Face Recognition
ClinicalTrials.gov study NCT06474923. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of Visual Mecanisms Involved in Face Recognition
ClinicalTrials.gov study NCT06851923. IPD Sharing: NO. Countries: 0. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.