The PASCAL FACE dataset is a dataset for face detection and face recognition. It has a total of 851 images which are a subset of the PASCAL VOC and has a total of 1,341 annotations. These datasets contain only a few hundreds of images and have limited variations in face appearance.

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This dataset contains a set of face images taken between April 1992 and April 1994 at AT&T Laboratories Cambridge.

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The VGG Face dataset is face identity recognition dataset that consists of 2,622 identities. It contains over 2.6 million images.

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…There are 105 celebrities and 17534 faces.

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High-resolution thermal infrared face database with extensive manual annotations, introduced by Kopaczka et al, 2018. Merhof, "A fully annotated thermal face database and its application for thermal facial expression recognition," 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018 A thermal infrared face database with facial landmarks and emotion labels. IEEE Transactions on Instrumentation and Measurement, 68(5), 1389-1401.

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IMDb-Face is large-scale noise-controlled dataset for face recognition research. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images.

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FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces. Selfreenactment: where the authors use Face2Face to reenact the facial expressions of videos with their own facial expressions as input to get pairs of videos, which e.g. can be used to train supervised

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FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino.

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MegaFace was a publicly available dataset which is used for evaluating the performance of face recognition algorithms with up to a million distractors (i.e., up to a million people who are not in the test MegaFace captures many different subjects rather than many images of a small number of subjects. The gallery set of MegaFace is collected from a subset of Flickr. Facescrub dataset contains more than 100K face images of 530 people. The MegaFace challenge evaluates performance of face recognition algorithms by increasing the numbers of “distractors” (going from 10 to 1M) in the gallery set. In order to evaluate the face recognition algorithms fairly, MegaFace challenge has two protocols including large or small training sets.

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The IDiff-Face dataset was proposed in the paper "IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models". This dataset is synthetically generated using the IDiff-Face model.

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FaceVerse-High Quality 3D Face Dataset contains 2,688 high-quality head scans (21 expressions from 128 identities) captured by a dense DLSR rig. For each scan, we provide the 3D model (.obj), the corresponding texture map (.jpeg) and the FaceVerse fitted model (.ply) with the same topology.

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A new face annotation dataset with balanced distribution between genders and ethnic origins.

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FaceOcc is a high-quality face occlusion dataset which contains all mislabeled occlusions in CelebAMask-HQ and complements some occlusions and textures from the internet.

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FaceForensics++ is a forensics dataset consisting of 1000 original video sequences that have been manipulated with four automated face manipulation methods: Deepfakes, Face2Face, FaceSwap and NeuralTextures The data has been sourced from 977 youtube videos and all videos contain a trackable mostly frontal face without occlusions which enables automated tampering methods to generate realistic forgeries.

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The CUHK Face Alignment Database is dataset with 13,466 face images, among which 5, 590 images are from LFW and the remaining 7, 876 images are downloaded from the web. Each face is labeled with the positions of five facial keypoints. 10,000 images are used for training and the remaining 3,466 images for validation. <paper>

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FaceWarehouse is a 3D facial expression database that provides the facial geometry of 150 subjects, covering a wide range of ages and ethnic backgrounds.

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FaceScape dataset provides 3D face models, parametric models and multi-view images in large-scale and high-quality. The camera parameters, the age and gender of the subjects are also included.

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MobiFace is the first dataset for single face tracking in mobile situations.

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TinyFace is a large scale face recognition benchmark to facilitate the investigation of natively LRFR (Low Resolution Face Recognition) 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. All the LR faces in TinyFace are collected from public web data across a large variety of imaging scenarios, captured under uncontrolled viewing conditions in pose, illumination, occlusion and background

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BeautyFace is a dataset containing 3,000 high-quality face images with a higher resolution of 512*512, covering more recent makeup styles and more diverse face poses, backgrounds, expressions, races, illumination Each face has annotated parsing map.

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KinFaceW consists of two kinship datasets: KinFaceW-I and KinFaceW-II. Face images were collected from the internet, including some public figure face images as well as their parents' or children's face images. Face images are captured under uncontrolled environments in two datasets with no restriction in terms of pose, lighting, background, expression, age, ethnicity, and partial occlusion. The difference of KinFaceW-I and KinFaceW-II is that face images with a kin relation were acquired from different photos in KinFaceW-I and the same photo in KinFaceW-II in most cases.

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A dog face dataset for dog face verification and recognition/identification.

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Proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net).

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Charlotte-ThermalFace is a thermal face dataset.

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…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 [Figure 2].

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The iCartoonFace dataset is a large-scale dataset that can be used for two different tasks: cartoon face detection and cartoon face recognition.

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…The cornerstone of our research is a rich collection of artificial celebrity faces, titled DeepFakeFace (DFF).

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…"The Chicago Face Database was developed at the University of Chicago by Debbie S. Ma, Joshua Correll, and Bernd Wittenbrink. The CFD is intended for use in scientific research. It provides high-resolution, standardized photographs of male and female faces of varying ethnicity between the ages of 17-65. Extensive norming data are available for each individual model. ., face size) as well as subjective ratings by independent judges (e.g., attractiveness).

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The free Face dataset made for students and teachers. It contains 10,000 photos with equal distribution of race and gender parameters, along with metadata and facial landmarks. Quantity 10,000 Quality 256x256px Diversity Ethnicity, gender Metadata The JSON files contain the metadata for each image in a machine-readable format, including: (1) FaceLandmarks: mouth, right_eyebrow , left_eyebrow, right_eye, left_eye, nose, jaw. (2) FaceAttributes: headPose, gender, makeup, emotion, facialHair, hair (hairColor, hairLength, bald), occlusion, ethnicity, eye_color, smile, age

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CASIA-Face-Africa is a face image database which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects , especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc.

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The CASIA-WebFace dataset is used for face verification and face identification tasks. The dataset contains 494,414 face images of 10,575 real identities collected from the web.

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QMUL-SurvFace is a surveillance face recognition benchmark that contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes over wide space and time

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The dataset contains patches of facial reflectance as described in the paper, namely the diffuse albedo, diffuse normals, specular albedo, specular normals, as well as the shape in UV space. For the shape, reconstructed meshes have been registered to a common topology and the XYZ values of the points have been mapped to the RGB in UV coordinates and interpolated to complete the UV map. From the complete UV maps of 6144x4096 pixels, patches of 512x512 pixels have been sampled. The dataset contains 7500 such patches (1500 of each datatype) that are anonymized, randomized and sampled so that they do not contain identifiable features.

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iFakeFaceDB is a face image dataset for the study of synthetic face manipulation detection, comprising about 87,000 synthetic face images generated by the Style-GAN model and transformed with the GANprintR

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The COVID-19 pandemic raises the problem of adapting face recognition systems to the new reality, where people may wear surgical masks to cover their noses and mouths. ., CelebA, CASIA-WebFace) used for training these systems were released before the pandemic, so they now seem unsuited due to the lack of examples of people wearing masks. We propose a method for enhancing data sets containing faces without masks by creating synthetic masks and overlaying them on faces in the original images. Our method relies on Spark AR Studio, a developer program made by Facebook that is used to create Instagram face filters. In our approach, we use 9 masks of different colors, shapes and fabrics. We employ our method to generate a number of 445,446 (90%) samples of masks for the CASIA-WebFace data set.

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KinFaceW-I dataset contains 533 pairs of facial images of persons with a kin relation. Each sample is composed of one parent face image and one child face image.

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…Train image classification model to classify anime characters by face image. This dataset includes 130 characters with 75 images each, scrapped from Danbooru.

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DigiFace-1M is a synthetic dataset for face recognition, obtained by rendering digital faces using a computer graphics pipeline. It contains 1.22M images of 110K unique identities. Following the format of the existing datasets, we provide the aligned crop around the face, resized into $112 \times 112$ resolution. Please visit the website for more detail.

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Toronto NeuroFace Dataset: A New Dataset for Facial Motion Analysis in Individuals with Neurological Disorders Toronto NeuroFace Dataset is a public dataset with videos of oro-facial gestures performed

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KinFaceW-II Dataset consists of 1000 pairs of facial images of individuals with a kin relation. Different from the KinFaceW-I database, the positive pairs in this dataset are taken from the same photo.

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WebFace260M is a million-scale face benchmark, which is constructed for the research community towards closing the data gap behind the industry. It consists of: - Noisy 4M identities and 260M faces - High-quality training data with 42M images of 2M identities by using automatic cleaning - A test set with rich attributes and a time-constrained evaluation

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RegDB is used for Visible-Infrared Re-ID which handles the cross-modality matching between the daytime visible and night-time infrared images. The dataset contains images of 412 people. It includes 10 color and 10 thermal images for each person.

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Description： 23 Pairs of Identical Twins Face Image Data. The collecting scenes includes indoor and outdoor scenes. The subjects are Chinese males and females. The data diversity inlcudes multiple face angles, multiple face postures, close-up of eyes, multiple light conditions and multiple age groups. This dataset can be used for tasks such as twins' face recognition.

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Celeb-HQ Face Gender Recognition Dataset This dataset is curated for the face gender classification task. The dataset contains 30,000 images. There are 23,999 train images. The whole face images are divided into two classes. There are 11,057 male images. There are 18,943 female images.

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Description: 1,995 People Face Images Data (Asian race). For each subject, more than 20 images per person with frontal face were collected. This data can be used for face recognition and other tasks. Data size: 1,995 people, more than 20 images per person with frontal face Race distribution: Asian people

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Description： 5,011 Images – Human Frontal face Data (Male). The data diversity includes multiple scenes, multiple ages and multiple races. This dataset includes 2,004 Caucasians , 3,007 Asians. This dataset can be used for tasks such as face detection, race detection, age detection, beard category classification.

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FAD is a dataset that have roughly 200,000 attribute labels for the above traits, for over 10,000 facial images.

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SF-MASK is a collection made from 20k low-resolution images exported from diverse and heterogeneous datasets, ranging from 7 x 7 to 64 x 64 pixel resolution. An accurate visualization of this collection, through counting grids, made it possible to highlight gaps in the variety of poses assumed by the heads of the pedestrians.

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