Face dataset

Face Detection Datasets & Databases - facial finding

Kinship Face Videos in the Wild (KFVW), a video face dataset, was collected from TV shows on the Web for the video-based kinship verification study. Compared to a still image, a video provides more information to describe the appearance of human face, because it can easily capture the face of the person of interest from different poses. The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large. HuggingFace Datasets¶. Datasets and evaluation metrics for natural language processing. Compatible with NumPy, Pandas, PyTorch and TensorFlow. Datasets is a lightweight and extensible library to easily share and access datasets and evaluation metrics for Natural Language Processing (NLP)

2. Applied mask-to-face deformable model and data outputs. The dataset of face images Flickr-Faces-HQ 3 (FFHQ) has been selected as a base for creating an enhanced dataset MaskedFace-Net composed of correctly and incorrectly masked face images. Indeed, FFHQ contains 70,000 high-quality images of human faces in PNG file format of 1024 × 1024 resolution and is publicly available The MUCT Face Database The Yale Face Database B The Yale Face Database PIE Database The UMIST Face Database Olivetti - Att - ORL The Japanese Female Facial Expression (JAFFE) Database The Human Scan Database The University of Oulu Physics-Based Face Database XM2VTSDB Databases with over 100 unique individuals in the CMU Face Images Data Set. Download: Data Folder, Data Set Description. Abstract: This data consists of 640 black and white face images of people taken with varying pose (straight, left, right, up), expression (neutral, happy, sad, angry), eyes (wearing sunglasses or not), and size. Data Set Characteristics: Image. Number of Instances: 640. Area Datasets. code. Code. comment. Discussions. school. Courses. expand_more. More. auto_awesome_motion. 0. View Active Events. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. By using Kaggle, you agree to our use of cookies. Got it. Learn more

3D Face Dataset. 3D face datasets are of great value in face-related research areas. Existing 3D face datasets could be categorized according to the acquisition of 3D face model. Model fitting datasets[33, 60, 23, 5, 7] fit the 3D morphable model to the collected images, which makes it convenient to build a large-scale dataset on the base of. Since many faces have multiple labeled attributes, face ids may be repeated (i.e., if a given face has two attributes labeled, then there will be two lines starting with that face id). facetracer.py: A simple python script that demonstrates how to parse the dataset and display information about a particular face in the dataset Makeup Datasets is a series of datasets of female face images assembled for studying the impact of makeup on face recognition. MIW (Makeup in the Wild) Dataset - There is one set of data, Makeup in the Wild that contains face images of subjects with and without makeup that were obtained from the internet SCface - Surveillance Cameras Face Database. Summary: SCface is a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database contains 4160 static images (in visible and infrared spectrum) of 130 subjects Google Facial Expression Comparison Dataset is a large-scale facial expression dataset that consists of face image triplets along with human annotations. The dataset helps in specifying which two faces in each triplet form the most similar pair in terms of facial expression. The dataset is intended to help on topics related to facial expression.

GitHub - blancaag/face-dataset

Face Mask Detection Dataset 7553 Images. Face Mask Detection Data set In recent trend in world wide Lockdowns due to COVID19 outbreak, as Face Mask is became mandatory for everyone while roaming outside, approach of Deep Learning for Detecting Faces With and Without mask were a good trendy practice Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset, which is 10 times larger than. The WIDER FACE dataset contains 32,203 images and labels 393,703 faces with a high degree of variability in scale, pose and occlusion. The database is split into training (40%), validation (10%) and testing (50%) set. Besides, the images are divided into three levels (Easy ⊆ Medium ⊆ Hard) according to the difficulties of the detection. The images and annotations of training and validation. We list some face databases widely used for face related studies, and summarize the specifications of these databases as below. 1. Caltech Occluded Face in the Wild (COFW). o Source: The COFW face dataset is built by California Institute of Technology, o Purpose: COFW face dataset contains images with severe facial occlusion. The images are.

10 Face Datasets To Start Facial Recognition Project

The face recognition scheme based on deep learning can give the best face recognition performance at present, but this scheme requires a large amount of labeled face data. The currently available large-scale face datasets are mainly Westerners, only containing few Asians Code. Results. Date. Stars. Masked Face Recognition Dataset and Application. 20 Mar 2020. 1,317. WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19 The dataset contains 10,301 face images of 1,018 identities. Each identity has masked and common face images with various orientations, lighting conditions and mask types. Most identities have 5 holistic face images and 5 masked face images with 5 different views: front, left, right, up and down. LICENS

DEAP: A Dataset for Emotion Analysis using Physiological

UTKFace Large Scale Face Datase

All the datasets currently available on the Hub can be listed using datasets.list_datasets (): To load a dataset from the Hub we use the datasets.load_dataset () command and give it the short name of the dataset you would like to load as listed above or on the Hub. Let's load the SQuAD dataset for Question Answering Faces dataset decompositions. ¶. This example applies to The Olivetti faces dataset different unsupervised matrix decomposition (dimension reduction) methods from the module sklearn.decomposition (see the documentation chapter Decomposing signals in components (matrix factorization problems)) . Out: Dataset consists of 400 faces Extracting the. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector

The ND-IIITD Retouched Faces database is a dataset of original face images and retouched versions of those face images. The database contains 2600 original images and 2275 altered images. It is meant for use in the problem of developing methods to classify a face image as original or retouched Description. WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset.We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes

The Asian Face Age Dataset (AFAD) is a new dataset proposed for evaluating the performance of age estimation, which contains more than 160K facial images and the corresponding age and gender labels. This dataset is oriented to age estimation on Asian faces, so all the facial images are for Asian faces JAFFE Face Database ORL Face Database CMU Face Database MIT-CBCL Face Database LFW Face Database I used five different databases for the testing of the RIFDS (Rotation Invariant Face Detection Software -face detection software) with detection accu.. These are the different descriptors and the file names. 5. meta_data.tar.gz (132MB, md5sum: ): Contains the meta_and_splits.mat file, which provides an easy way for accessing the mat files in the descriptors DB. The Splits is a data structure dividing the data set to 10 independent splits The FAce Semantic SEGmentation repository View on GitHub Download .zip Download .tar.gz. Welcome to the webpage of the FAce Semantic SEGmentation (FASSEG) repository.. The FASSEG repository is composed by two datasets (frontal01 and frontal02) for frontal face segmentation, and one dataset (multipose01) with labaled faces in multiple poses.If you use our datasets, please cite our works ([1] or. CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. It has substantial pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary.

Face Recognition Homepage - Database

  1. ological Face Dataset With Image Metadata. FACEMETA is the largest commercially available dataset of facial images with detailed metadata. The FACEMETA dataset is intended for use in academic research and corporate R&D. It contains 100,000 normalized photographs of male and female faces of varying ethnicity between the ages of.
  2. This dataset is a collection of JPEG pictures of famous people collected over the internet, all details are available on the official website: Each picture is centered on a single face. The typical task is called Face Verification: given a pair of two pictures, a binary classifier must predict whether the two images are from the same person. An.
  3. Dimensions like face symmetry, facial contrast, the pose the face is in, the length or width of the face's attributes (eyes, nose, forehead, etc.) are also important. Today, IBM Research is releasing a new large and diverse dataset called Diversity in Faces (DiF) to advance the study of fairness and accuracy in facial recognition technology
  4. Ukiyo-e faces dataset. Last touched October 29, 2020. Download the dataset: V2. As part of my paper Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains rdgi I use a dataset of Ukiyo-e face images for training a StyleGAN model, this post contains a link to, and details of that dataset.. Updates. V2 - Removed 28 bad quality images (poor alignment or not face)

WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is. Introduction. The PubFig database is a large, real-world face dataset consisting of 58,797 images of 200 people collected from the internet. Unlike most other existing face datasets, these images are taken in completely uncontrolled situations with non-cooperative subjects. Thus, there is large variation in pose, lighting, expression, scene. Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. However, most of the large datasets are maintained by private companies and are not publicly available. The academic computer vision community needs larger and more varied datasets to make further progress.

The dataset focuses on a specific challenge of face recognition under the disguise covariate. According to the DFW's description, it covers disguise variations for hairstyles, beard, mustache, glasses, make-up, caps, hats, turbans, veils, masquerades and ball masks. This is coupled with other variations for pose, lighting, expression. Face and CASIA datasets [13][20]. IJB-C includes a total of 31,334 (21,294 face and 10,040 non-face) still images, averaging to ˘6 images per subject, and 117,542 frames from 11,779 full-motion videos, aver-aging to ˘33 frames per subject and ˘3 videos per subject. The contributions of the IJB-C dataset to face recognitio Multivariate, Text, Domain-Theory . Classification, Clustering . Real . 2500 . 10000 . 201

The dataset consists of 1521 gray level images with a resolution of 384×286 pixel. Each one shows the frontal view of a face of one out of 23 different test persons. For comparison reasons the set also contains manually set eye postions Dataset 07: CBSR NIR Face Dataset [NIR_face_dataset.zip] (NIR face dataset) [gallery-groundtruth.txt] (gallery ground truth) [probe-groundtruth.txt] (probe ground truth) Dataset includes 3,940 NIR face images of 197 persons. The image size is 480 by 640 pixels, 8 bit, without compression. The 3,940 images are divided into a gallery set and a. Face Dataset and other potentially trademarked words, copyrighted images and copyrighted readme contents likely belong to the legal entity who owns the Jian667 organization. Awesome Open Source is not affiliated with the legal entity who owns the Jian667 organization The most covert dataset available to date Details: • Contains 6,337 visible images from 308 subjects • Images captured at a distance of roughly 100m • Dataset is ideal for re-identification scenario Sapkota, Archana, and Terrance E. Boult. Large scale unconstrained open set face database. Biometrics: Theory

A development data set, which contains several hundreds of face images and ground truth labels will be provided to the participants for self-evaluations and verifications. Please note that above datasets are all optional to be used. That is, systems that based on MS-Celeb-1M and/or any other private/public datasets will all be evaluated for. datasets can easily occur due to biased selection, capture, and negative sets [60]. Most public large scale face datasets have been collected from popular online media - newspa-pers, Wikipedia, or web search- and these platforms are more frequently used by or showing White people. To mitigate the race bias in the existing face datasets, w Our face dataset is designed to present faces in real-world conditions. Faces show large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.g. food, hands, microphones Large face datasets are important for advancing face recogni-tion research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we describe an approach to building face datasets that starts with detecting faces in images returne The MegaFace dataset is the largest publicly available facial recognition dataset with a million faces and their respective bounding boxes. All images obtained from Flickr (Yahoo's dataset) and licensed under Creative Commons

GitHub - jian667/face-dataset: Face related dataset

MaskTheFace can be used to convert any existing face dataset to a masked-face dataset. MaskTheFace identifies all the faces within an image and applies the user-selected masks to them taking into account various limitations such as face angle, mask fit, lighting conditions, etc. A single image or entire directory of images can be used as input. Let's create a dataset class for our face landmarks dataset. We will read the csv in __init__ but leave the reading of images to __getitem__. This is memory efficient because all the images are not stored in the memory at once but read as required. Sample of our dataset will be a dict {'image': image, 'landmarks': landmarks} As AI advances, and humans and AI systems increasingly work together, it is essential that we trust the output of these systems to inform our decisions. Alongside policy considerations and business efforts, science has a central role to play: developing and applying tools to wire AI systems for trust. IBM Research's comprehensive strategy addresses multiple dimensions of trust to enable AI. Microsoft Celeb. Microsoft Celeb (MS-Celeb-1M) is a dataset of 10 million face images harvested from the Internet for the purpose of developing face recognition technologies. According to Microsoft Research, who created and published the dataset in 2016, MS Celeb is the largest publicly available face recognition dataset in the world. I am working on implementing a face detection model on the wider face dataset. I learned it was built into Tensorflow datasets and I am using it. However, I am facing an issue while batching the data. Since, an Image can have multiple faces, therefore the number of bounding boxes output are different for each Image

VGG Face Dataset - University of Oxfor

GENDER-COLOR-FERET dataset is a balanced subset of the COLOR-FERET dataset, adapted for gender recogntion purposes. In this case the images are coloured and the dataset is composed by 836 faces. The dataset is completely balanced, since both the training and the test set are composed of 209 male and 209 female faces Visible-Thermal Face (ARL-VTF) dataset. This dataset is, to the best of our knowledge, the largest thermal face dataset publicly available for scientific research to date. The main contributions of the ARL-VTF dataset are: A multi-modal, time synchronized acquisition of 395 subjects and over 500,000 face images captured usin IMDb Dataset Details. Each dataset is contained in a gzipped, tab-separated-values (TSV) formatted file in the UTF-8 character set. The first line in each file contains headers that describe what is in each column. A '\N' is used to denote that a particular field is missing or null for that title/name Synopsis. The Yale Face Database (size 6.4MB) contains 165 grayscale images in GIF format of 15 individuals. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink

Datasets - KinFaceW: Kinship Face in the Wild databas

Before training, however, we need to process this dataset to categorize and normalize the data. In this article, we'll create a dataset parser/processor and run it on the Yale Face dataset, which contains 165 grayscale images of 15 different people. This dataset is small but sufficient for our purpose - learning. Prepare a Parse The dataset has infrared facial images, 3D depth images, and images of the visible light spectrum for each sample. •. The data can be used to train classifiers or artificial neural networks to identify a face, age or gender. Different classifiers can be trained to identify the best type of image for a given issue, such as facial recognition

[2008.08016] MaskedFace-Net -- A Dataset of Correctly ..

  1. WIDER FACE is a face detection benchmark dataset with 32,203 images and 393,703 annotated faces. Both of these datasets only have protocols designed for face detection, and thus cannot be used to evaluate face verification or identification directly. The release of the NIST Face Challenge [6] and the IARPA Janus Benchmark A (IJB-A) dataset [9.
  2. The WIDER FACE dataset contains annotations for 393,703 faces spread over 32,203 images. The annotations include bounding box for the face, pose (typical/atypical), and occlusion level (partial/heavy). FDDB has been driving a lot of progress in face detection in recent years. It has annotations for 5,171 faces in 2,845 images
  3. A million faces for face recognition at scale. MegaFace is the largest publicly available facial recognition dataset. Toggle navigation. MegaFace. MegaFace and MF2: Million-Scale Face Recognition. The MegaFace challenge has concluded, reaching a benchmark performance of over 99%. Because its goals have been met, and ongoing maintenance of this.
  4. The proposed facial mask segmentation model is trained with pairs of RGB images and its corresponding alpha image created by extending the publicly available real-world masked face dataset. Further, the proposed model is pruned and optimized using the TensorRt library to be usable for real-world applications
  5. The use of dataset for face recognition usually uses images of photos originated from single media such as dataset from mobile phone [1,2], Facebook , digital camera [4,5]. Algorithm development for face recognition requires images dataset from various media sources, it is a challenge for researchers because the expected results in face.
  6. Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard face recognition datasets. One example of a state-of-the-art model is the VGGFace and VGGFace2 model developed by researchers at the.

The goal of the sponsored research was to develop face recognition algorithms. The FERET database was collected to support the sponsored research and the FERET evaluations. The FERET evaluations were performed to measure progress in algorithm development and identify future research directions Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to multiple third-party open source implementations o This dataset is a large-scale facial expression dataset consisting of face image triplets along with human annotations that specify which two faces in each triplet form the most similar pair in terms of facial expression. Each triplet in this dataset was annotated by six or more human raters

HuggingFace Datasets — datasets 1

  1. MegaFace is a large-scale public face recognition training dataset that serves as one of the most important benchmarks for commercial face recognition vendors. It includes 4,753,320 faces of 672,057 identities from 3,311,471 photos downloaded from 48,383 Flickr users' photo albums. All photos included a Creative Commons licenses, but most were.
  2. ated images are generally uncommon in these datasets
  3. The first known comprehensive heterogeneous face database is created that includes many different types of image modalities: photographic images, a computerized facial sketch created from a portrait sitting of the participant using the FACEs software, thermal images, near infrared (NIR) images, and a 3D image of the participant
  4. The FaceScrub dataset was created using this approach, followed by manually checking and cleaning the results. It comprises a total of 106,863 face images* of male and female 530 celebrities, with about 200 images per person. As such, it is one of the largest public face databases. The dataset was also used as part of the MegaFace face.
  5. 3D facial models have been extensively used for 3D face recognition and 3D face animation, the usefulness of such data for 3D facial expression recognition is unknown. To foster the research in this field, we created a 3D facial expression database (called BU-3DFE database), which includes 100 subjects with 2500 facial expression models
Detecting cats in images with OpenCV - PyImageSearchHigh Resolution Passive Facial Performance Capture

We create a large scale face recognition benchmark, named TinyFace, to facilitate the investigation of natively LRFR at large scales (large gallery population sizes) in deep learning. The TinyFace dataset consists of 5,139 labelled facial identities given by 169,403 native LR face images (average 20×16 pixels) designed for 1:N recognition test Following are some of the popular sites where you can find datasets related to facial expressions http://www.consortium.ri.cmu.edu/ckagree/ - neutral, sadness.

Female Face 20s FullFace #12 – Texturing

MaskedFace-Net - A dataset of correctly/incorrectly masked

The FEI face database is a Brazilian face database that contains a set of face images taken between June 2005 and March 2006 at the Artificial Intelligence Laboratory of FEI in São Bernardo do Campo, São Paulo, Brazil. There are 14 images for each of 200 individuals, a total of 2800 images This dataset contains 625 facial videos from 125 individuals. Total number of videos covering the face and torso of each individual is 5. Each video has been captured at a resolution of (1280 x 720) at 30 fps and the duration of each video ranges from 40 to 60 seconds. Sensor used - LOGITECH WEBCAM HD720p. Signatures

Face Database Info - MI

The dataset consists of high-resolution 3D scans of human faces from each subject, along with several video sequences of varying resolution and zoom level. Each subject is recorded in a controlled setting in HD video, then in a less-constrained (but still indoor) setting using a standard, PTZ surveillance camera, and finally in an unconstrained. Danbooru2019 Portraits is a dataset of n = 302,652 (16GB) 512px anime faces cropped from solo SFW Danbooru2019 images in a relatively broad 'portrait' style encompassing necklines/ ears/ hats/ etc rather than tightly focused on the face, upscaled to 512px as necessary, and low-quality images deleted by manual review using 'Discriminator ranking', which has been used for creating TWDNE ⁠ The WIDER FACE dataset is a face detection benchmark dataset, where images are selected from the open source WIDER dataset. 32k images are chosen and 394k faces annotated with a high degree of variability in scale, pose and occlusion in the sample images. WIDER FACE dataset is organized based on 61 event classes, which are randomly split in a train/val/testing split of 40/10/50

UCI Machine Learning Repository: CMU Face Images Data Se

  1. If you want a real face dataset, I strongly recommend the UMass project: Labelled Faces in the Wild. Frey Face [data/frey_rawface.mat] From Brendan Frey. Almost 2000 images of Brendan's face, taken from sequential frames of a small video. Size: 20x28. Olivetti Faces [data/olivettifaces.mat
  2. Description. In order to facilitate the study of age and gender recognition, we provide a data set and benchmark of face photos. The data included in this collection is intended to be as true as possible to the challenges of real-world imaging conditions. In particular, it attempts to capture all the variations in appearance, noise, pose.
  3. FREE FLIR Thermal Dataset for Algorithm Training. The FLIR starter thermal dataset enables developers to start training convolutional neural networks (CNN), empowering the automotive community to create the next generation of safer and more efficient ADAS and driverless vehicle systems using cost-effective thermal cameras from FLIR
  4. The dataset contains images of people collected from the web by typing common given names into Google Image Search. The coordinates of the eyes, the nose and the center of the mouth for each frontal face are provided in a ground truth file. This information can be used to align and crop the human faces or as a ground truth for a face detection.
  5. Datasets consisting primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification. Facial recognition. In computer vision, face images have been used extensively to develop facial recognition systems, face detection, and many other projects that use images of faces
  6. DFFD: Diverse Fake Face Dataset. The prevalence of facial recognition, biometric unlock, and social media presents a significant opportunity for bad actors to introduce forged or manipulated images to spread false information or damage reputations. This is aided by the continuing improvement in realistic image synthesis and manipulation by.

Wearing face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. Hence, a large dataset of masked faces is necessary for training deep learning models towards detect This video is about Face Verification Dataset. This video is about Face Verification Dataset Lets Do Face Recognition. To make a face recognition program, first we need to train the recognizer with dataset of previously captured faces along with its ID, for example we have two person then first person will have ID 1 and 2nd person will have ID 2, so that all the images of person one in the dataset will have ID 1 and all the images of the 2nd person in the dataset will have ID 2, then.


Deepfake Detection Challenge Kaggl

  1. In addition, the dataset comes with the manual landmarks of 6 positions in the face: left eye, right eye, the tip of nose, left side of mouth, right side of mouth and the chin. Other information of the person such as gender, year of birth, glasses (this person wears the glasses or not), capture time of each session are also available
  2. Static Face Images for all the identities in VoxCeleb2 can be found in the VGGFace2 dataset. If you require text annotation (e.g. for audio-visual speech recognition), also consider using the LRS dataset. Emotion labels obtained using an automatic classifier can be found for the faces in VoxCeleb1 here as part of the 'EmoVoxCeleb' dataset
  3. In each *.face file, it contains a 4-by-n matrix, where n is the frame length. Each row records the (x,y) locations of the upper left corner and the botton right corner of the current bounding box, such as [785 425 1070 710]. Sign the Dataset Release Agreement (DRA). 2
  4. The DFDC dataset is by far the largest currently- and publicly-available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned methods. In addition to describing the methods used to construct the dataset, we provide a detailed analysis of the top.
  5. 3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction. Yuda Qiu 1,2 Xiaojie Xu 2 Lingteng Qiu 1,2 Yan Pan 1,2 Yushuang Wu 1,2 Weikai Chen 3 Xiaoguang Han# 1,2* * Corresponding email: hanxiaoguang@cuhk.edu.cn 1 The Chinese University of Hong Kong, Shenzhen 2 Shenzhen Research Institute of Big Data 3 Tencent Game AI Research Cente
  6. ation conditions. The data format of this database is the same as the Yale Face Database B
  7. imum of 4 images per subject (2 before-makeup images and 2 after-makeup images). Some subjects have 6 images (3 before-makeup images and 3 after-makeup.

Dataset. UDIVA v0.5 (ICCV'21) Large Scale Signer Independent Isolated SLR Dataset (CVPR'21) UDIVA; 3D+Texture garment reconstruction (NeurIPS'20) Fair Face Recognition (ECCV'20) Identity-preserved Human Detection (FG'20) Face Anti-Spoofing (CVPR'19) Fingerprint inpainting and denoising (WCCI'18, ECCV'18) Video Decaptioning (WCCI'18, ECCV'18 The normalized yale face database / The original dataset in PGM format. matlab/ Code used to process the original YALE dataset rotated/ Faces rotated so eyes are aligned horizontally centered/ Rotated faces cropped and middle of eyes centered. unpadded/ Centered faces cropped out supported/ Unpadded faces shrunk and outline blanked out. This face database was created by Aleix Martinez and Robert Benavente in the Computer Vision Center (CVC) at the U.A.B. It contains over 4,000 color images corresponding to 126 people's faces (70 men and 56 women). This database contains human subjects who agreed to participate in the adquisition of this dataset for research purposes. To. The Mask Wearing dataset is an object detection dataset of individuals wearing various types of masks and those without masks. The images were originally collected by Cheng Hsun Teng from Eden Social Welfare Foundation, Taiwan and relabled by the Roboflow team. Example image (some with masks, some without)

Homer Simpson defeats Google's all-powerful DeepMind

UMDAA-02 Face Dataset (UMDAA-02-FD): A sampled version of the front-camera image captures of the UMDAA-02 dataset are annotated for face Detection and verification tasks. The annotated dataset can be downloaded from here. Face detection and verification results on this data can be found in the following papers Face images found in LFW Dataset. Basically our goal is building a dataset like LFW, however, we're gonna have pictures with and without people wearing masks In the previous article of this series, we labeled a face mask dataset. Now is the time for the most exciting part of this project - the model training. Preparing the Training and Validation Data. Just like any other model, YOLOv5 needs some validation data to determine how good the inferences are during and after training Among them, to the best of our knowledge, RMFRD is currently theworld's largest real-world masked face dataset. These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed. The multi-granularity masked face recognition model we developed achieves 95% accuracy, exceeding the. The dataset metadata and features used in this paper can be downloaded [] (4.4G)The dataset metadata only can be downloaded [] (817K)Original face images (detected and croped by openCV face detector) can be downloaded [] (3.5G)16 faical landmark locations used in this paper can be downloaded [] (24M) (extracted by Intraface)Note The proposed facial mask segmentation model is trained with pairs of RGB images and its corresponding alpha image created by extending the publicly available real-world masked face dataset. Further, the proposed model is pruned and optimized using the TensorRt library to be usable for real-world applications