3D Face Reconstruction

FaMoS is a dynamic 3D head dataset from 95 subjects, each performing 28 motion sequences. The sequences comprise of six prototypical expressions (i.e., Anger, Disgust, Fear, Happiness, Sadness, and Surprise), two head rotations (left/right and up/down), and diverse facial motions, including extreme and asymmetric expressions. Each sequence is recorded at 60 fps. In total, FaMoS contains around 600K 3D head meshes (i.e., ~225 frames per sequence). For each frame, registrations in FLAME meshes are publicly available.

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

KANFace consists of 40K still images and 44K sequences (14.5M video frames in total) captured in unconstrained, real-world conditions from 1,045 subjects. The dataset is manually annotated in terms of identity, exact age, gender and kinship. Source: KANFace Dataset

MFW+ is a benchmark dataset for masked face recognition and an extended version of MFW. The original MFW, published as a benchmark for masked face recognition, is composed of 300 IDs and 3,000 images. However, with two duplicate IDs found in MFW, the dataset actually contains 298 unique IDs and 2,980 images. To evaluate models under various mask conditions and environments, we manually gathered additional data from the web. The refined and extended MFW, which we named MFW+, contains 606 IDs, 2,911 unmasked face images, and 2,838 masked face images. Paper: https://bmvc2022.mpi-inf.mpg.de/0723.pdf

PetFace is a large-scale animal face re-identification dataset that includes 257,484 unique individuals across 13 families and 319 breeds. PetFace has fine-grained annotation (sex, breeds, color, and patterns).

SKSF-A consists of seven distinct styles drawn by professional artists. SKSF-A contains 134 identities and corresponding sketches, making a total of 938 face-sketch pairs. SKSF-A is introduced in StyleSketch, Eurographics 2024. https://kwanyun.github.io/stylesketch_project/

SuHiFiMask (Surveillance High-Fidelity Mask) extends FAS to real surveillance scenes rather than mimicking low-resolution images and surveillance environments. It contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Source: Surveillance Face Presentation Attack Detection Challenge

Comprised of real human and wax figure images and videos that endorse the problem of face spoofing detection. The dataset consists of more than 1800 face images and 110 videos of 55 people/waxworks, arranged in training, validation and test sets with a large range in expression, illumination and pose variations. Source: The SWAX Benchmark: Attacking Biometric Systems with Wax Figures

VideoForensicsHQ is a benchmark dataset for face video forgery detection, providing high quality visual manipulations. It is one of the first face video manipulation benchmark sets that also contains audio and thus complements existing datasets along a new challenging dimension. VideoForensicsHQ shows manipulations at much higher video quality and resolution, and shows manipulations that are provably much harder to detect by humans than videos in other datasets. VideoForensicsHQ contains 1,737 videos of speaking faces (44% male, 56% female), with 8 different emotions, most of them of “HD” resolution. The videos amount to 1,666,816 frames. Source: VideoForensicsHQ: Detecting High-quality Manipulated Face Videos

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

A View From Somewhere (AVFS)—a dataset of 638,180 face similarity judgments over 4,921 faces. Each judgment corresponds to the odd-one-out (i.e., least similar) face in a triplet of faces and is accompanied by both the identifier and demographic attributes of the annotator who made the judgment.

EEG signals from 60 users have been recorded whose age range lies between 6 and 55 years. Among all, there were 25 females and 35 male users. In general, all the participants were either school children or belonged to the socioeconomic cross section of the population with no medical history. The EEG recordings were acquired from all 14 electrodes operating at a sampling rate of 128 Hz. During recording, the participants were asked to comfortably sit on the chair with clear thoughts and a relaxed state.

Although research on author profiling has quite progressed in abundant resources languages, it is still infancy for limited resources languages such as Bengali. This repository contains our Bangla Author Profiling Dataset (BN-AuthProf). The primary objective is to introduce and benchmark the performance of machine learning approaches on Age and Gender Classification tasks from the social media status of people. BN-AuthProf dataset consists of 300 anonymized authors and 30,131 manually curated Facebook posts in Bangla, tagged with age and gender information.

The CANDOR corpus is a large, novel, multimodal corpus of 1,656 recorded conversations in spoken English. This 7+ million word, 850 hour corpus totals over 1TB of audio, video, and transcripts, with moment-to-moment measures of vocal, facial, and semantic expression, along with an extensive survey of speaker post conversation reflections.

A large-scale, first-of-its-kind database aimed at generating a better understanding of the way children interact with mobile devices during their development process. ChildCIdbv1 comprises data collected from 438 children, from 18 months to 8 years old, encompassing the first three development stages of Piaget's theory. Data collected spans interaction with screens using both finger and pen stylus, information regarding the previous experience of the child with mobile devices, the child’s grade level, and whether attention-deficit/hyperactivity disorder (ADHD) is present. Use cases: Child age detection based on device interaction.

The Curated AFD dataset is a curated version of the Asian Face Dataset (AFD) for face recognition research. The original AFD dataset has been curated to remove wrong identity labels, duplicate images and duplicate subjects. Source: https://arxiv.org/abs/2004.03074

The proposed Extended-YouTube Faces (E-YTF) is an extension of the famous YouTube Faces (YTF) dataset and is specifically designed to further push the challenges of face recognition by addressing the problem of open-set face identification from heterogeneous data i.e. still images vs video.

Facial Skeletal Angles (Glabella and Maxilla Angle and Length and Width of Piriformis)

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

Tables of the blendshapes from a group of the images of the FER2013 dataset, generated using MediaPipe library, based on the ARKit face blendshapes. with classes of the images in a separate column, describing the categories Happy, Unknown, Sad.

The FLUXSynID Dataset comprises 14,889 high-resolution synthetic face identities, each uniquely represented with paired images: document-style images and trusted live-capture images. The document-style images are designed to simulate ID photos, while the live-capture images are generated to reflect real-world conditions, including variations in lighting, facial expressions, and camera angles. This dual representation facilitates robust benchmarking for face recognition, morphing attack detection, and cross-domain identity verification tasks. FLUXSynID leverages cutting-edge generative models to ensure high fidelity and diversity across identities, enabling comprehensive analysis of biometric systems under varied conditions. For more information and to generate your own custom datasets using the FLUXSynID framework, visit the FLUXSynID GitHub repository.

Indian Masked faces in the wild Database is collected into three sets:(i) Indian Celebrity, (ii) Instagram and (iii) Indian Crowd. The Indian Celebrity contains 40 Indian celebrities with 435 images, including Bollywood actors/actresses, television stars, sports personalities, and politicians. The Instagram set contains 377 images of 40 subjects downloaded from Instagram. We collected masked and non-masked images of Indian people with a public profile. The Indian Crowd set is collected from the common people who volunteered to contribute to the dataset. This set contains 120 subjects with 562 images. All the Images are collected in both constrained and unconstrained environments with variation in pose, illumination, background and masks worn by the people.

InaGVAD is a Voice Activity Detection (VAD) and Speaker Gender Segmentation (SGS) dataset designed for representing the acoustic diversity of French TV and Radio programs. InaGVAD detailed description, together with a benchmark of 6 freely available VAD systems and 3 SGS systems, is provided in a paper presented in LREC-COLING 2024. InaGVAD contains 277 1-minute-long annotated recordings, partitioned into a 1h development and 3h37 test subset, allowing fair and reproducible system evaluation. Evaluation scripts provided with the corpus provide performance estimates in the same conditions as the 6 VAD and 3 SGS systems presented in the associated paper. Recordings were collected from 10 French radio and 18 TV channels categorized into 4 groups associated to diverse acoustic conditions : generalist radio, music radio, news TV, and generalist TV. InaGVAD provides an extended VAD and SGS annotation scheme, allowing to describe systems diverse abilities based on : * Speaker Traits categories ** 3 Genders : Female, Male, I Don't Know (IDK) ** 3 Age groups : Young (prepubescent), Adult, Ederly (Senior) ** 3 Speech Qualities : standard, interjections (ah, oh, eg, aie), atypical (crying, laughing or shouted speech, ill person voice, artificially distorted voices, vocal performance, monster voice...) *10 Non-Speech event categories : Applause, environmental noise, hubbub, jingle, foreground music, background music, respiration, non-intelligible laughers, other, empty The entire inaGVAD package; including corpus, annotations, evaluation scripts, and baseline training code; is made freely accessible, fostering future advancement in the domain.

"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. These data include both physical attributes (e.g., face size) as well as subjective ratings by independent judges (e.g., attractiveness). Detailed information about the construction of the database and the available norming data can be found in Ma, Correll, & Wittenbrink (2015)."

An ancestral origin database of 14,000 images of individuals from East Asia, the Indian subcontinent, sub-Saharan Africa, and Western Europe. Source: Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings

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.

During the covid-19 era wearing face masks posed new challenges to face-related tasks, including facial recognition, face inpainting, expression recognition, and object removal. Mask region segmentation is a preliminary stage to tackle the occlusion issue corresponding to the face-related tasks. Existing masked face datasets are not procedure binary segmentation maps because Segmenting mask regions manually is a time-consuming operation. As a result, existing unmasking methods; synthesize training data by overlaying masks on existing face datasets. However, since these techniques rely on an artificially generated mask, their effects tend to seem unnatural. To address this issue, the masked face segmentation dataset(MFSD) provides the first public training dataset for the mask segmentation task.

MFW+ is a benchmark dataset for masked face recognition and an extended version of MFW. The original MFW, published as a benchmark for masked face recognition, is composed of 300 IDs and 3,000 images. However, with two duplicate IDs found in MFW, the dataset actually contains 298 unique IDs and 2,980 images. To evaluate models under various mask conditions and environments, we manually gathered additional data from the web. The refined and extended MFW, which we named MFW+, contains 606 IDs, 2,911 unmasked face images, and 2,838 masked face images. Paper: https://bmvc2022.mpi-inf.mpg.de/0723.pdf

This dataset provides a collection of 162K images and 70 Videos of Meta-Humans. There are 10 Highly realistic Meta-Humans expressing 7 facial expressions.

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