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Dataset results
19 results for “face detection”
OpenForensics: Multi-Face Forgery Detection And Segmentation In-The-Wild Dataset [V.1.0.0]
<p>OpenForensics is the first large-scale dataset posing a high level of challenges. This dataset is designed with face-wise rich annotations explicitly for face forgery detection and segmentation. With its rich annotations, OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. Project Page: https://sites.google.com/view/ltnghia/research/openforensics</p>
Diverse Dataset for Eyeglasses Detection: Extending the Flickr-Faces-HQ (FFHQ) Dataset
<p>Extension of the FFHQ dataset (https://github.com/NVlabs/ffhq-dataset) with precise bounding box annotations for eyeglasses detection.</p> <p>If you find this dataset useful in your research, please consider citing the original dataset and the following papers:</p> <p>@article{matuzevicius2024diverse,<br> title={Diverse Dataset for Eyeglasses Detection: Extending the Flickr-Faces-HQ (FFHQ) Dataset},<br> author={Matuzevi{\v{c}}ius, Dalius},<br> journal={Sensors},<br> volume={24},<br> number={23},<br> pages={7697},<br> year={2024},<br> publisher={MDPI},<br> url = {https://doi.org/10.3390/s24237697}<br>}</p> <p>@article{matuzevicius2024retrospective,<br> title={A Retrospective Analysis of Automated Image Labeling for Eyewear Detection Using Zero-Shot Object Detectors},<br> author={Matuzevi{\v{c}}ius, Dalius},<br> journal={Electronics},<br> volume={13},<br> number={23},<br> pages={4763},<br> year={2024},<br> publisher={MDPI},<br> url = {https://doi.org/10.3390/electronics13234763}<br>}</p> <p> </p>
TrueFace: a Dataset for the Detection of Synthetic Face Images from Social Networks
<p>TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram.</p> <p>Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.</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>
Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning
<p>The <em><strong>EAVISE Open Source Face Detection Dataset</strong></em> consists of several items that were used to generate the improved frontal face detection model using LBP features and AdaBoost for OpenCV3.2.</p> <ul> <li>The annotations of the FDDB dataset, converted to the OpenCV format for doing a correct evaluation.</li> <li>The final trained model (IterativeHardPositives+ model) which is included in the OpenCV 3.2 framework.</li> </ul>
TriModal Face Detection Dataset
<p>TMFD Dataset addresses the lack of available depth and thermal face detection benchmarks. The corresponding depth and thermal data frames accompany each RGB image in the dataset. The dataset encompasses a wide range of variations. The samples include different numbers of people in the scene, different individuals, various types of backgrounds, varying distances of measurement, and wearable accessories worn by the individuals, such as hoodies, headphones, hats, glasses, and face masks. Additionally, the dataset includes different types of illumination in the scene. The dataset is categorized into three separate groups based on the complexity and difficulty level of face detection. The first subset represents the easiest detection conditions, with images containing a single person against a simple background. The sensor is positioned close to the target. This subset consists of 781 images for each modality. The second group represents medium detection conditions. The sensor placement remains the same as in the first subset, but additional variety is introduced. Individuals are captured wearing accessories that serve as obstacles to make detection more challenging. These accessories include hoodies, headphones, hats, and glasses. The second group also includes images with multiple persons in the scene to test the performance of multiple face detection. There are 965 images for each modality in this group. The last group represents the most challenging images for detection. In this case, the sensor is placed further away from the target. The scenes feature various obstacles, such as computer screens, plants, desks, and chairs, with individuals interacting with them. The presence of singular and plural individuals is also included. A distinct subset within this collection consists of images from a previous, easier scene, but with the addition of face mask usage to increase the difficulty of the prediction task. This particular group contains 1062 images for each modality.</p> <p> </p> <p>This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to:</p> <ul> <li>Mucha W., Kampel M. (2022) “Depth and Thermal Images in Face Detection – A Detailed Comparison Between Image Modalities”, The 5th International Conference on Machine Vision and Applications (ICMVA 2022), February 18-20, 2022, Singapore <a href="https://doi.org/10.1145/3523111.3523114">https://doi.org/10.1145/3523111.3523114</a></li> </ul>
Data belonging to paper 'Detecting Faces, Medium Types, Gender in Historical Advertisments'
<p>This dataset contains annotations, intermediate data, and outputted models for the VISART paper <em>'Detecting Faces, Medium Types, Gender in Historical Advertisments</em>.'</p> <p> </p> <p>See the <a href="https://github.com/melvinwevers/detecting_faces-medium-types-gender">repo</a> on GitHub for info on how to use this data.</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>
An automated system for inspecting rock faces and detecting potential rock falls using machine learning
<p>Rockfall is a hazard in mountainous areas threatening infrastructure and human lives. Rockfall hazards are often mitigated by manual inspections using pry bars. The inspector must access the rock face, hit the rock surface, detect, and remove the loose rocks. This method is very labor demanding, unsafe, and challenging. This research presents a method that automatize the inspection of rock blocks that are prone to rockfall events. A robot is developed to replace the manual hammer tap process and collect the sound data remotely; subsequently, the sound signal is used to identify different types of the discontinuity in rocks in controlled laboratory environment. Machine learning is used to train the method to discriminate between intact rock and rock that may be prone to fall. This methodology was successfully applied to laboratory tests on rock. Finally, the research involves the implementation of this system in field to understand the potential and limitations of the proposing system in automatizing the rock inspections. This research enables the inspectors to collect data remotely, detect loose rocks, and save data for future references.</p>
Dataset for "Face Detection in Untrained Deep Neural Networks"
<p><strong>Dataset for</strong></p> <p><strong>"Face Detection in Untrained Deep Neural Networks"</strong></p> <p>Seungdae Baek, Min Song, Jaeson Jang, Gwangsu Kim, and Se-Bum Paik*</p> <p>*Contact: <a href="mailto:sbpaik@kaist.ac.kr">sbpaik@kaist.ac.kr</a></p> <p> </p> <p>To run demo codes for "Face Detection in Untrained Deep Neural Networks", please download files below.</p> <p> </p> <p><strong>1. Stimulus.zip</strong></p> <p>- <strong>IMG_cntr_210521.mat</strong> : A low-level feature-controlled stimulus set was used to find units that responded selectively to face images (Stigliani, 2015). Specifically, 260 images were prepared for each class (face, hand, horn, flower, chair, and scrambled face).</p> <p>- <strong>IMG_var_pos/size/rot_210521.mat</strong> : To investigate the invariance of face-selective units to face images of various sizes, positions, and rotation angles, the image set (Stigliani, 2015) was generated after modifying the size, position, and rotation angle of the faces and other objects in the low-level feature-controlled stimulus set.</p> <p>- <strong>IMG_var_view_210106.mat</strong> : This set was used to find units that invariantly responded to face images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-90°, -45°, 0°, 45°, 90°) with ten different faces obtained from (Gourier 2004)</p> <p> </p> <p><strong>2. Data.zip</strong></p> <p>- <strong>Data_PFI_XDream/RevCorr_ClsUnit.mat</strong> : The obtained preferred feature images, using reverse-correlation method and X-Dream, of units selective to each object class.</p> <p>- <strong>Data_SVM_NeuronType.mat</strong> : Simulated face detection performance using distinct types of selective units in Conv5 and using the shuffled responses of face-selective units in untrained networks (a. all selective units, b. units selective to non-face classes (nonface-selective), c. face-selective units, d. units selective to none of these classes (non-selective)).</p> <p>- <strong>Data_SVM_invariance.mat</strong> : Face detection performance with variation of the low-level features. </p> <p>- <strong>PretrainedNet</strong> : Sample networks was untrained and trained with three types of image sets (a. face-reduced ImageNet, b. original ImageNet, c. original ImageNet with added face images (Stigliani, 2015)) (number of networks = 3). </p> <p> - Net_Untrained : untrained AlexNets<br> - Net_FD_ImageNet : AlexNets trained to face-reduced ImageNet<br> - Net_ImageNet : AlexNets trained to original ImageNet<br> - Net_ImageNetwFace : AlexNets trained to original ImageNet with added face images</p> <p>- <strong>Data_Trained.mat</strong> : Result summary of four types of networks above (number of networks = 10).</p> <p> </p>
Face detection dataset perturbed using Gimp's foggify filterwith turbulence=4.0 and opacity 100.
<p>This dataset is a perturbed version of the dataset https://zenodo.org/record/3879989#.Y-HBCXbMI2w</p> <p>Please refer to https://zenodo.org/record/3879989#.Y-HBCXbMI2w for eye coordinates and depth maps. </p>
Face detection ensemble with methods using depth information to filter false positives
<p>%files (File.rar):<br> %ColorImageX.jpg is the color image<br> %SegImageX.jpg is the segmentation map <br> %SegImage2_X.jpg is the segmentation map obtained considering only Depth<br> %map</p> <p>%the .mat file<br> %DatasetFaceD{x}{1} depth map<br> %DatasetFaceD{x}{2} eyes coordinates<br> %DatasetFaceD{x}{3} calibration matrix, e.g used in face_dimension.m</p> <p> </p> <p>PS few images (7) used in the paper are no more available</p> <p> </p>
Investigation of the Accordance Between Event Detection of prismaLINE Devices and Polysomnography Using Full Face Masks
ClinicalTrials.gov study NCT06317077. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Detection of Circulating Kidney DNA in Kidney Transplant Patients Facing an Episode of Graft Rejection
ClinicalTrials.gov study NCT06351488. IPD Sharing: Not stated. Countries: 1. Publications: 0.
" En Face " OCT's Contribution in the Detection of Macular Retinal Toxicity Signs Linked to the Chronic Treatment of Synthetic Antimalarials
ClinicalTrials.gov study NCT02805686. IPD Sharing: Not stated. Countries: 0. Publications: 0.
in-Vehicle Face Presentation Attack Detection (VFPAD)
<p><strong>Description</strong></p> <p>The in-Vehicle Face Presentation Attack Detection (VFPAD) dataset consists of 4046 bona-fide recordings from 40 subjects, and 1790 attack presentation videos from a total of 89 PAIs (presentation attack instruments). These presentations have been captured using an NIR camera (940 nm) placed on the steering wheel of the car, while NIR illuminators have been fixed on both front pillars (adjacent to the wind-shield) of the car. The bona-fide videos represent 24 male and 16 female subjects of various ethnicities. The PAI species used to construct this dataset include photo-prints, digital displays (for replay attacks), rigid 3D masks, and flexible 3D masks made of silicone.</p> <p> </p> <p><strong>Data Collection</strong></p> <p>The videos comprising this dataset represent bona-fide and attack presentations under a range of variations:</p> <ul> <li>Environmental variations: presentations have been recorded in four sessions, each under different environmental conditions (outdoor sunny; outdoor cloudy; indoor dimly-lit; and indoor brightly-lit)</li> <li>Different scenarios: bona-fide presentations for each subject have been captured with variety of appearances: with/without glasses, with/without hat, etc.</li> <li>Illumination variations: two illumination conditions have been used: ‘uniform’ (both NIR illuminators switched on), and ‘non-uniform’ (only the left NIR-illuminator switched on), and</li> <li>Pose variations: two poses (‘angles’) have been used: ‘front’: the subject looks ahead at the road; and ‘below’: subject looks straight into the camera.</li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>If you use the dataset, please cite the following publication:</p> <p>@article{IEEE_TBIOM_2021,<br> author = {Kotwal, Ketan and Bhattacharjee, Sushil and Abbet, Philip and Mostaani, Zohreh and Wei, Huang and Wenkang, Xu and Yaxi, Zhao and Marcel, S\'{e}bastien},<br> title = {Domain-Specific Adaptation of CNN for Detecting Face Presentation Attacks in NIR},<br> journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},<br> publisher = {{IEEE}},<br> year={2022},<br> volume={4},<br> number={1},<br> pages={135--147},<br> doi={10.1109/TBIOM.2022.3143569}<br> }</p>
Face Mask Detection Dataset
<p>The dataset contains real images of people with face mask and without face mask. Each class contains 150 images.</p> <p>Cite us:</p> <p>Ferdib-Al-Islam, Suprio Sarkar, Nusrat Jahan and Farjana Yeasmin Rupa, “Face Mask Detection Dataset”. Zenodo, Jul. 15, 2021. doi: 10.5281/zenodo.5305989</p>
ScienceDex guides
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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.