Face Detection

The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels. There are totally 150 semantic categories, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. Source: Cooperative Image Segmentation and Restoration in Adverse Environmental Conditions Image Source: https://groups.csail.mit.edu/vision/datasets/ADE20K/

Manga109 has been compiled by the Aizawa Yamasaki Matsui Laboratory, Department of Information and Communication Engineering, the Graduate School of Information Science and Technology, the University of Tokyo. The compilation is intended for use in academic research on the media processing of Japanese manga. Manga109 is composed of 109 manga volumes drawn by professional manga artists in Japan. These manga were commercially made available to the public between the 1970s and 2010s, and encompass a wide range of target readerships and genres (see the table in Explore for further details.) Most of the manga in the compilation are available at the manga library “Manga Library Z” (formerly the “Zeppan Manga Toshokan” library of out-of-print manga). Source: Manga109 Image Source: https://arxiv.org/pdf/1510.04389v1.pdf

The Face Detection Dataset and Benchmark (FDDB) dataset is a collection of labeled faces from Faces in the Wild dataset. It contains a total of 5171 face annotations, where images are also of various resolution, e.g. 363x450 and 229x410. The dataset incorporates a range of challenges, including difficult pose angles, out-of-focus faces and low resolution. Both greyscale and color images are included. Source: A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications

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

UMDFaces is a face dataset divided into two parts: * Still Images - 367,888 face annotations for 8,277 subjects. * Video Frames - Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects. Part 1 - Still Images The dataset contains 367,888 face annotations for 8,277 subjects divided into 3 batches. The annotations contain human curated bounding boxes for faces and estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network. Part 2 - Video Frames The second part contains 3,735,476 annotated video frames extracted from a total of 22,075 for 3,107 subjects. The annotations contain the estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network.

COCO-WholeBody is an extension of COCO dataset with whole-body annotations. There are 4 types of bounding boxes (person box, face box, left-hand box, and right-hand box) and 133 keypoints (17 for body, 6 for feet, 68 for face and 42 for hands) annotations for each person in the image. Source: Whole-Body Human Pose Estimation in the Wild Image source: https://arxiv.org/pdf/2007.11858v1.pdf

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. Source: Pushing the Limits of Unconstrained Face Detection:a Challenge Dataset and Baseline Results Image Source: https://www.researchgate.net/figure/Precision-recall-curves-on-PASCAL-face-dataset_fig7_332998926

WIDER is a dataset for complex event recognition from static images. As of v0.1, it contains 61 event categories and around 50574 images annotated with event class labels.

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). Source: MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19

The MALF dataset is a large dataset with 5,250 images annotated with multiple facial attributes and it is specifically constructed for fine grained evaluation. Source: Pushing the Limits of Unconstrained Face Detection:a Challenge Dataset and Baseline Results Image Source: http://www.cbsr.ia.ac.cn/faceevaluation/

Unconstrained Face Detection Dataset (UFDD) aims to fuel further research in unconstrained face detection. Source: Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results Image Source: https://ufdd.info/

Real-World Masked Face Dataset (RMFD) is a large dataset for masked face detection. Source: https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset Image Source: https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset

The iCartoonFace dataset is a large-scale dataset that can be used for two different tasks: cartoon face detection and cartoon face recognition. Source: https://github.com/luxiangju-PersonAI/iCartoonFace Image Source: https://github.com/luxiangju-PersonAI/iCartoonFace

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

Contains video clips shot with modern high-resolution mobile cameras, with strong projective distortions and with low lighting conditions. Source: MIDV-2019: Challenges of the modern mobile-based document OCR

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

The DCM dataset is composed of 772 annotated images from 27 golden age comic books. We freely collected them from the free public domain collection of digitized comic books Digital Comics Museum. One album per available publisher was selected to get as many different styles as possible. We made ground-truth bounding boxes of all panels, all characters (body + faces), small or big, human-like or animal-like. Image source: https://gitlab.univ-lr.fr/crigau02/dcm-dataset/-/tree/master

BAFMD contains images posted on Twitter during the pandemic from around the world with more images from underrepresented race and age groups to mitigate the problem for the face mask detection task. Source: Bias-Aware Face Mask Detection Dataset Image Source: https://arxiv.org/pdf/2211.01207v1.pdf

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. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. 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.

Multimodal Dyadic Behavior (MMDB) dataset is a unique collection of multimodal (video, audio, and physiological) recordings of the social and communicative behavior of toddlers. The MMDB contains 160 sessions of 3-5 minute semi-structured play interaction between a trained adult examiner and a child between the age of 15 and 30 months. The MMDB dataset supports a novel problem domain for activity recognition, which consists of the decoding of dyadic social interactions between adults and children in a developmental context. Source: MMDB

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. Data size： 5,011 people, one image per person Race distribution： 2,004 Caucasians , 3,007 Asians

DARK FACE dataset provides 6,000 real-world low light images captured during the nighttime, at teaching buildings, streets, bridges, overpasses, parks etc., all labeled with bounding boxes for of human face, as the main training and/or validation sets. We also provide 9,000 unlabeled low-light images collected from the same setting. Additionally, we provided a unique set of 789 paired low-light/normal-light images captured in controllable real lighting conditions (but unnecessarily containing faces), which can be used as parts of the training data at the participants' discretization. There will be a hold-out testing set of 4,000 low-light images, with human face bounding boxes annotated.

A 360-degree fisheye-like version of the popular FDDB face detection dataset. Source: FDDB-360: Face Detection in 360-degree Fisheye Images

The Hochschule Darmstadt (HDA) facial tattoo and paintings database contains 500 pairs of facial images of individuals with and without facial tattoos or paintings. The database was collected from multiple online sources.

The Human-Parts dataset is a dataset for human body, face and hand detection with ~15k images. It contains ~106k different annotations, with multiple annotations per image. Source: https://github.com/xiaojie1017/Human-Parts Image Source: https://github.com/xiaojie1017/Human-Parts

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. Data size： 23 pairs, each person in a pair of identical twins has 40 images (20 indoor images, 20 outdoor images) Population distribution： race distribution: Asian (Chinese); gender distribution: male 9 pairs, female 14 pairs; age distribution: 12 pairs under 18 years old, 10 pairs aged from 18 to 40, 1 pairs over 40 years old

FAD is a dataset that have roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. Source: Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information

Consists of a large number of unconstrained multi-view and partially occluded faces. Source: Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild

Dataset originally conceived for multi-face tracking/detection for highly crowded scenarios. In these scenarios, the face is the only part that can be used to track the individuals. All our videos present novel crowd scenes recorded at near-eye level, where faces are visible enough to be analysed at the microscopic level, while also benefiting from a macroscopic view of the crowd. It includes: - Face detections of 715 unique subjects along with instructions to download the synchronized video. - More than 75k face detections annotated. - A density ranging from 3 to 13 people/frame. - 6 indoor and 4 outdoor videos. 8/10 videos are totally unconstrained, 2/10 feature 3 re-appearances per subject. Our dataset may be useful for: - Face tracking, especially relevant for crowded scenarios (typically from video-surveillance cameras). - Heavily occluded body tracking (in many videos, only the face is mostly visible). - Face recognition. - Face detection for partially occluded faces.

MobiFace is the first dataset for single face tracking in mobile situations. It consists of 80 unedited live-streaming mobile videos captured by 70 different smartphone users in fully unconstrained environments. Over 95K bounding boxes are manually labelled. The videos are carefully selected to cover typical smartphone usage. The videos are also annotated with 14 attributes, including 6 newly proposed attributes and 8 commonly seen in object tracking. Source: MobiFace

In the last two years, millions of lives have been lost due to COVID-19. Despite the vaccination programmes for a year, hospitalization rates and deaths are still high due to the new variants of COVID-19. Stringent guidelines and COVID-19 screening measures such as temperature check and mask check at all public places are helping reduce the spread of COVID-19. Visual inspections to ensure these screening measures can be taxing and erroneous. Automated inspection ensures an effective and accurate screening. To perform automated screening, thermal based screening is effective as it is illumination independent and can work even under no lighting conditions. This NTIC screening dataset consists of thermal images of persons walking into public premises like offices, malls and railway stations. The ground truth consists of annotations of human faces and whether they are masks or not. Broadly, this dataset is divided into 3 sub-datasets: Thermal Surveillance Dataset: 902 thermal images with 1354 people wearing masks and 213 people without masks Augmented Surveillance Dataset: 543 images with 434 people wearing masks and 109 people without masks Lighting Dataset: 420 thermal images and their corresponding visual images.

A occluded version of the LFW dataset for occluded face recognition verification. Uses structured occlusions generated to seem more realistic.

Procedural Human Action Videos contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Source: Procedural Generation of Videos to Train Deep Action Recognition Networks

In this paper, we introduce a victim dataset for the RoboCup Rescue competitions. The RoboCup Rescue robots have to collect points within several disciplines, e.g. a search task within an area to survey simulated baby doll (victim). When a robot comes across a victim, a heat detector does not completely proof if this is a living being and not just a heat emitting somewhat else. Further investigations are necessary so that a face detection could prove the existence of a victim. Lots of face detection approaches can be found in literature, which manly are used for human face recognition. These cannot be straightforward used for victim faces which are, in case of the RoboCup Rescue competitions, typically dolls. Thus we present the results of standard approaches and developed an own approach via bag-of-visual-words (BoVW).

Unconstrained Face Detection and Open-Set Face Recognition Challenge * Paper: https://arxiv.org/abs/1708.02337 * Official website: https://vast.uccs.edu/Opensetface * UnOfficial website: https://exposing.ai/uccs Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.

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. Free to use for research with citation Photos by Generated.Photos. Photos All the photos are 100% synthetic. Based on model-released photos. Royalty-free. Can be used for any research purpose except for the ones violating the law. Worldwide. No time limitations. 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