Face Alignment

CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age. Source: Show, Attend and Translate: Unpaired Multi-Domain Image-to-Image Translation with Visual Attention Image Source: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

The 300-W is a face dataset that consists of 300 Indoor and 300 Outdoor in-the-wild images. It covers a large variation of identity, expression, illumination conditions, pose, occlusion and face size. The images were downloaded from google.com by making queries such as “party”, “conference”, “protests”, “football” and “celebrities”. Compared to the rest of in-the-wild datasets, the 300-W database contains a larger percentage of partially-occluded images and covers more expressions than the common “neutral” or “smile”, such as “surprise” or “scream”. Images were annotated with the 68-point mark-up using a semi-automatic methodology. The images of the database were carefully selected so that they represent a characteristic sample of challenging but natural face instances under totally unconstrained conditions. Thus, methods that achieve accurate performance on the 300-W database can demonstrate the same accuracy in most realistic cases. Many images of the database contain more than one annotated faces (293 images with 1 face, 53 images with 2 faces and 53 images with [3, 7] faces). Consequently, the database consists of 600 annotated face instances, but 399 unique images. Finally, there is a large variety of face sizes. Specifically, 49.3% of the faces have size in the range [48.6k, 2.0M] and the overall mean size is 85k (about 292 × 292) pixels. Source: https://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_2016_imavis.pdf Image Source: https://www.researchgate.net/profile/Xuanyi_Dong/publication/323722412/figure/fig1/AS:679426136227845@1538999222829/Face-samples-from-300-W-dataset-Different-faces-have-different-styles-whereas-the-style_Q640.jpg

The HELEN dataset is composed of 2330 face images of 400×400 pixels with labeled facial components generated through manually-annotated contours along eyes, eyebrows, nose, lips and jawline. Source: Face Parsing via a Fully-Convolutional Continuous CRF Neural Network Image Source: http://www.ifp.illinois.edu/~vuongle2/helen/

The Annotated Facial Landmarks in the Wild (AFLW) is a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25K faces are annotated with up to 21 landmarks per image. Source: Nose, Eyes and Ears: Head Pose Estimation by Locating Facial Keypoints

AFW (Annotated Faces in the Wild) is a face detection dataset that contains 205 images with 468 faces. Each face image is labeled with at most 6 landmarks with visibility labels, as well as a bounding box. Source: Face detection, pose estimation, and landmark localization in the wild

The Labeled Face Parts in-the-Wild (LFPW) consists of 1,432 faces from images downloaded from the web using simple text queries on sites such as google.com, flickr.com, and yahoo.com. Each image was labeled by three MTurk workers, and 29 fiducial points, shown below, are included in dataset. Source: https://neerajkumar.org/databases/lfpw/ Image Source: https://neerajkumar.org/databases/lfpw/

AFLW2000-3D is a dataset of 2000 images that have been annotated with image-level 68-point 3D facial landmarks. This dataset is used for evaluation of 3D facial landmark detection models. The head poses are very diverse and often hard to be detected by a CNN-based face detector. Source: https://www.tensorflow.org/datasets/catalog/aflw2k3d Image Source: http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm

The Caltech Occluded Faces in the Wild (COFW) 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, etc.). All images were hand annotated using the same 29 landmarks as in LFPW. Both the landmark positions as well as their occluded/unoccluded state were annotated. The faces are occluded to different degrees, with large variations in the type of occlusions encountered. COFW has an average occlusion of over 23. Source: http://www.vision.caltech.edu/xpburgos/ICCV13/#dataset Image Source: http://www.vision.caltech.edu/xpburgos/ICCV13/#dataset

The Wider Facial Landmarks in the Wild or WFLW database contains 10000 faces (7500 for training and 2500 for testing) with 98 annotated landmarks. This database also features rich attribute annotations in terms of occlusion, head pose, make-up, illumination, blur and expressions. Source: Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion Image Source: https://wywu.github.io/projects/LAB/WFLW.html

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. The data have been released to public for non-commercial research purpose. Source: FaceScape

The original AFLW provides at most 21 points for each face, but excluding coordinates for invisible landmarks, causing difficulties for training most of the existing baseline approaches. To make fair comparisons, the authors manually annotate the coordinates of these invisible landmarks to enable training of those baseline approaches. The new annotation does not include two ear points because it is very difficult to decide the location of invisible ears. This causes the point number of AFLW-19 to be 19. The original AFLW does not provide train-test partition. AFLW-19 adopts a partition with 20,000 images for training and 4,386 images for testing (AFLW-Full). In addition, a frontal subset (AFLW-Frontal) is proposed where all landmarks are visible (totally 1,165 images). The new 19-point annotation file is available at the project page.

A 3D facial landmark dataset of around 230,000 images. Source: How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

3DFAW contains 23k images with 66 3D face keypoint annotations. Source: Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild Image Source: http://mhug.disi.unitn.it/workshop/3dfaw/

A large-scale, hierarchical annotated dataset of animal faces, featuring 21.9K faces from 334 diverse species and 21 animal orders across biological taxonomy. These faces are captured `in-the-wild' conditions and are consistently annotated with 9 landmarks on key facial features. The proposed dataset is structured and scalable by design; its development underwent four systematic stages involving rigorous, manual annotation effort of over 6K man-hours. Source: AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

The SAMM Long Videos dataset consists of 147 long videos with 343 macro-expressions and 159 micro-expressions. The dataset is FACS-coded with detailed Action Units. Source: SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset

300 Videos in the Wild (300-VW) is a dataset for evaluating facial landmark tracking algorithms in the wild. The dataset authors collected a large number of long facial videos recorded in the wild. Each video has duration of ~1 minute (at 25-30 fps). All frames have been annotated with regards to the same mark-up (i.e. set of facial landmarks) used in the 300 W competition as well (a total of 68 landmarks). The dataset includes 114 videos (circa 1 min each).

The MERL-RAV (MERL Reannotation of AFLW with Visibility) Dataset contains over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded (due to extreme head poses), or externally occluded. The images were annotated by professional labelers, supervised by researchers at Mitsubishi Electric Research Laboratories (MERL).