Head Pose Estimation

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

CMU Panoptic is a large scale dataset providing 3D pose annotations (1.5 millions) for multiple people engaging social activities. It contains 65 videos (5.5 hours) with multi-view annotations, but only 17 of them are in multi-person scenario and have the camera parameters. Massively Multiview System * 480 VGA camera views * 30+ HD views * 10 RGB-D sensors * Hardware-based sync * Calibration * Interesting Scenes with Labels Multiple people * Socially interacting groups * 3D body pose * 3D facial landmarks * Transcripts + speaker ID Hardware setup * 480 VGA cameras, 640 x 480 resolution, 25 fps, synchronized among themselves using a hardware clock * 31 HD cameras, 1920 x 1080 resolution, 30 fps, synchronized among themselves using a hardware clock, timing aligned with VGA cameras * 10 Kinect Ⅱ Sensors. 1920 x 1080 (RGB), 512 x 424 (depth), 30 fps, timing aligned among themselves and other sensors 5 DLP Projectors. synchronized with HD cameras Source: Single-Stage Multi-Person Pose Machines Image Source: http://domedb.perception.cs.cmu.edu/

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

The dataset contains over 15K images of 20 people (6 females and 14 males - 4 people were recorded twice). For each frame, a depth image, the corresponding rgb image (both 640x480 pixels), and the annotation is provided. The head pose range covers about +-75 degrees yaw and +-60 degrees pitch. Ground truth is provided in the form of the 3D location of the head and its rotation. Source: https://www.kaggle.com/kmader/biwi-kinect-head-pose-database Image Source: https://icu.ee.ethz.ch/research/datsets.html

Biwi Kinect Head Pose is a challenging dataset mainly inspired by the automotive setup. It is acquired with the Microsoft Kinect sensor, a structured IR light device. It contains about 15k frame, with RGB. (640 × 480) and depth maps (640 × 480). Twenty subjects have been involved in the recordings: four of them were recorded twice, for a total of 24 sequences. The ground truth of yaw, pitch and roll angles is reported together with the head center and the calibration matrix. Source: POSEidon: Face-from-Depth for Driver Pose Estimation

These images were generated using Blender and IEE-Simulator with different head-poses, where the images are labelled according to nine classes (straight, turned bottom-left, turned left, turned top-left, turned bottom-right, turned right, turned top-right, reclined, looking up). The dataset contains 16,013 training images and 2,825 testing images, in addition to 4,700 images for improvements.

DAD-3DHeads dataset consists of 44,898 images collected from various sources (37,840 in the training set, 4,312 in the validation set, and 2,746 in the test set). The dataset is well-balanced over a wide range of poses, face expressions, and occlusions. The attribute labels are a valuable signal for subgroup analysis and for generalisation to in-the-wild deployment conditions.

The ARKitFace dataset is established by this work in order to train and evaluate both 3D face shape and 6DoF in the setting of perspective projection. A total of 500 volunteers, aged 9 to 60, are invited to record the dataset. They sit in a random environment, and the 3D acquisition equipment is fixed in front of them, with a distance ranging from about 0.3m to 0.9m. Each subject is asked to perform 33 specific expressions with two head movements (from looking left to looking right / from looking up to looking down). 3D acquisition equipment we used is an iPhone 11. The shape and location of human face are tracked by structured light sensor. The triangle mesh and 6DoF information of the RGB images are obtained by built-in ARKit toolbox. The triangle mesh is made up of 1,220 vertices and 2,304 triangles. In total, 902,724 2D facial images (resolution 1280×720 or 1440×1280) with ground-truth 3D mesh and 6DoF pose annotation are collected.

ICT-3DHP is collected using the Microsoft Kinect sensor and contains RGB images and depth maps of about 14k frames, divided in 10 sequences. The image resolution is 640 × 480 pixels. An hardware sensor (Polhemus Fastrack) is exploited to generate the ground truth annotation. The device is placed on a white cap worn by each subject, visible in both RGB and depth frames. Source: POSEidon: Face-from-Depth for Driver Pose Estimation