Face Recognition

MCXFace is a heterogeneous face recognition dataset consisting of multi-channel image samples for 51 subjects. For each subject color (RGB), thermal, near-infrared (850 nm), short-wave infrared (1300 nm), Depth, Stereo depth, and depth estimated from RGB images are available. Overall 7406 images together with landmark annotations and standard protocols are available in this dataset. The Multi-Channel Heterogeneous Face Recognition dataset (MCXFace) is derived from the HQ-WMCA dataset (https://www.idiap.ch/en/dataset/hq-wmca). The MCXFace dataset contains images of 51 subjects collected in different channels under three different sessions and various illumination conditions. The channels available are color (RGB), thermal, near-infrared (850 nm), short-wave infrared (1300 nm), Depth, Stereo depth, and depth estimated from RGB images using the 3DDFA method. All the channels are registered spatially and temporally across all the modalities. In the MCXFace dataset, only bonafide samples are present. Further, the files are divided into train and dev sets with a disjoint set of identities to make experiments in different homogeneous and heterogeneous settings possible. For each of the protocols, we have created five different folds, by randomly dividing the subjects into train and dev partitions. In addition to the images, annotations for left and right eye centers for all the images are also provided in a JSON file. Specifically for each image, annotations for the left eye, right eye, mouth left corner, mouth right corner, nose, top left, and top right are provided. Each image file is stored as a ``.jpg'' file with a resolution of 1920x1200.

A multimodal database for eye blink detection and attention level estimation. Source: mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation

The Masked LFW (MLFW), based on Cross-Age LFW (CALFW) database, is built using a simple but effective tool that generates masked faces from unmasked faces automatically. Image source: https://arxiv.org/pdf/2109.05804v1.pdf

Aims to facilitate research in caricature recognition. All the caricatures and face images were collected from the Web. Compared with two existing datasets, this dataset is much more challenging, with a much greater number of available images, artistic styles and larger intra-personal variations. Source: WebCaricature: a benchmark for caricature recognition

Large, multimodal biometric dataset: It contains still images and videos of over 1,000 people captured at various ranges (up to 1,000 meters) and elevations (up to 400 meters) using a diverse set of cameras (commercial, military-grade, specialized). Focus on challenging scenarios: Designed to address limitations of existing datasets, it includes data with extreme poses, low resolutions, and atmospheric distortions to push the boundaries of biometric recognition algorithms. Rich annotations and diversity: Each data point is accompanied by detailed metadata (sensor details, weather conditions) and manual annotations (bounding boxes, yaw/pitch angles) for face and whole-body regions. The dataset also ensures demographic diversity for robust algorithm development.

Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA). Due to its subjective nature, it is necessary to estimate and guarantee the consistency of the IQA process, a characteristic lacking in the mean opinion scores (MOS) widely used for crowdsourcing IQA. In addition, existing blind IQA (BIQA) datasets pay little attention to the difficulty of cross-content assessment, which may degrade the quality of annotations. This paper introduces PIQ23, a portrait-specific IQA dataset of 5116 images of 50 predefined scenarios acquired by 100 smartphones, covering a high variety of brands, models, and use cases. The dataset includes individuals of various genders and ethnicities who have given explicit and informed consent for their photographs to be used in public research. It is annotated by pairwise comparisons (PWC) collected from over 30 image quality experts for three image attributes: face detail preservation, face target exposure, and overall image quality. An in-depth statistical analysis of these annotations allows us to evaluate their consistency over PIQ23. Finally, we show through an extensive comparison with existing baselines that semantic information (image context) can be used to improve IQA predictions. The dataset along with the proposed statistical analysis and BIQA algorithms are available: https://github.com/DXOMARKResearch/PIQ2023

ROF is a dataset for occluded face recognition that contains faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks.

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 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. Traditional data sets (e.g., 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.

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. Traditional data sets (e.g., 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 196,254 (96.8%) masks for the CelebA data set.

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.

Paper Abstract We present IJB–S dataset, an open-source IARPA Janus Surveillance Video Benchmark and associated protocols. The dataset consists of images and surveillance video collected from 202 subjects at a Department of Defense (DoD) training facility. Surveillance video was captured across multiple vignettes representative of a variety of real-world surveillance use cases that are particularly of interest to law enforcement and national security communities. Each video was annotated by human subject matter experts in order to generate ground truth identity and bounding box face labels. In total, over 10 million annotations were collected for the dataset.

We have cleaned the noisy IMDB-WIKI dataset using a constrained clustering method, resulting this new benchmark for in-the-wild age estimation. The annotations also allow this dataset to use for some other tasks, like gender classification and face recognition/verification. For more details, please refer to our FPAge paper.

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

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. * There are 6,001 test images. * The whole face images are divided into two classes. * There are 11,057 male images. * There are 18,943 female images.

Celeb-HQ Facial Identity Recognition Dataset * This dataset is curated for the facial identity classification task. * There are 307 identities (celebrities). * Each identity has 15 or more images. * The dataset contains 5,478 images. * There are 4,263 training images. * There are 1,215 test images.

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

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

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

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.

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

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.

"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)."

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

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

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.

WildestFaces is tailored to study cross-domain recognition under a variety of adverse conditions. Source: Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos

The scales of the data accessible through internet search engines can reach hundreds of millions, or even billions. The existence of such large weak-labeled databases has gained importance in the training of face recognition algorithms. Starting with the publicly available YFCC100M, we propose a weakly-labeled subset for multi-label face recognition for self-supervised methods. A 392K image subset of YFCC100M of 128x128 images was obtained by querying for the 40 facial attributes. We made this dataset publicly available.

We introduce an annotated dataset of five thousand human labeled pareidolic face images, called ``Faces in Things''. Faces in Things is derived from the LAION-5B dataset and annotated for key face attributes and bounding boxes