Face Recognition

The LFW dataset contains 13,233 images of faces collected from the web. This dataset consists of the 5749 identities with 1680 people with two or more images. In the standard LFW evaluation protocol the verification accuracies are reported on 6000 face pairs. Source: A Performance Evaluation of Loss Functions for Deep Face Recognition Image Source: http://vis-www.cs.umass.edu/lfw

The CASIA-WebFace dataset is used for face verification and face identification tasks. The dataset contains 494,414 face images of 10,575 real identities collected from the web. Source: On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs

The MS-Celeb-1M dataset is a large-scale face recognition dataset consists of 100K identities, and each identity has about 100 facial images. The original identity labels are obtained automatically from webpages. NOTE: This dataset is currently inactive. Source: Learning to Cluster Faces on an Affinity Graph Image Source: MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

The IJB-C dataset is a video-based face recognition dataset. It is an extension of the IJB-A dataset with about 138,000 face images, 11,000 face videos, and 10,000 non-face images. Source: Pushing the Limits of Unconstrained Face Detection:a Challenge Dataset and Baseline Results Image Source: https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf

The Extended Yale B database contains 2414 frontal-face images with size 192×168 over 38 subjects and about 64 images per subject. The images were captured under different lighting conditions and various facial expressions. Source: Learning Locality-Constrained Collaborative Representation for Robust Face Recognition Image Source: http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html

MORPH is a facial age estimation dataset, which contains 55,134 facial images of 13,617 subjects ranging from 16 to 77 years old. Source: Deep Ordinal Regression Forests Image Source: https://uncw.edu/oic/tech/morph.html

The IJB-B dataset is a template-based face dataset that contains 1845 subjects with 11,754 images, 55,025 frames and 7,011 videos where a template consists of a varying number of still images and video frames from different sources. These images and videos are collected from the Internet and are totally unconstrained, with large variations in pose, illumination, image quality etc. In addition, the dataset comes with protocols for 1-to-1 template-based face verification, 1-to-N template-based open-set face identification, and 1-to-N open-set video face identification. Source: An Automatic System for Unconstrained Video-Based Face Recognition Image Source: https://www.vislab.ucr.edu/Biometrics2017/program_slides/Noblis_CVPRW_IJBB.pdf

The VGG Face dataset is face identity recognition dataset that consists of 2,622 identities. It contains over 2.6 million images. Source: https://www.robots.ox.ac.uk/~vgg/data/vgg_face/ Image Source: http://www.bmva.org/bmvc/2015/papers/paper041/paper041.pdf

To validate the racial bias of four commercial APIs and four state-of-the-art (SOTA) algorithms. Source: Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network

FGNet is a dataset for age estimation and face recognition across ages. It is composed of a total of 1,002 images of 82 people with age range from 0 to 69 and an age gap up to 45 years Source: Large age-gap face verification by feature injection in deep networks Image Source: https://www.researchgate.net/figure/Sample-images-from-the-FG-NET-Aging-database_fig1_220057621

CASIA-FASD is a small face anti-spoofing dataset containing 50 subjects. Source: Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing Image Source: https://arxiv.org/abs/1511.06316

Partial REID is a specially designed partial person reidentification dataset that includes 600 images from 60 people, with 5 full-body images and 5 occluded images per person. These images were collected on a university campus by 6 cameras from different viewpoints, backgrounds and different types of occlusion. The examples of partial persons in the Partial REID dataset are shown in the Figure. Source: Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification Image Source: https://arxiv.org/abs/1810.07399

The color FERET database is a dataset for face recognition. It contains 11,338 color images of size 512×768 pixels captured in a semi-controlled environment with 13 different poses from 994 subjects. Source: A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition Image Source: https://www.researchgate.net/figure/Sample-results-taken-from-Color-FERET-data-set-testing-using-LBP-algorithm_fig4_308836179

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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.

TinyFace is a large scale face recognition benchmark to facilitate the investigation of natively LRFR (Low Resolution Face Recognition) at large scales (large gallery population sizes) in deep learning. The TinyFace dataset consists of 5,139 labelled facial identities given by 169,403 native LR face images (average 20×16 pixels) designed for 1:N recognition test. All the LR faces in TinyFace are collected from public web data across a large variety of imaging scenarios, captured under uncontrolled viewing conditions in pose, illumination, occlusion and background.

CelebA-Spoof is a large-scale face anti-spoofing dataset with the following properties: 1. Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. 2. Diversity: The spoof images are captured from 8 scenes (2 environments * 4 illumination conditions) with more than 10 sensors. 3. Annotation Richness: CelebA-Spoof contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset. Source: CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations

Dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of subjects with videos and each sample has modalities (i.e., RGB, Depth and IR). Source: CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing

IMDb-Face is large-scale noise-controlled dataset for face recognition research. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. All images are obtained from the IMDb website. Source: The Devil of Face Recognition is in the Noise

DigiFace-1M is a synthetic dataset for face recognition, obtained by rendering digital faces using a computer graphics pipeline. It contains 1.22M images of 110K unique identities. The dataset consists of two parts. The first part contains 720K images with 10K identities. For each identity, 4 different sets of accessories are sampled and 18 images are rendered for each set. The second part contains 500K images with 100K identities. For each identity, only one set of accessories is sampled and only 5 images are rendered. Following the format of the existing datasets, we provide the aligned crop around the face, resized into $112 \times 112$ resolution. Please visit the website for more detail.

WebFace260M is a million-scale face benchmark, which is constructed for the research community towards closing the data gap behind the industry. It consists of: - Noisy 4M identities and 260M faces - High-quality training data with 42M images of 2M identities by using automatic cleaning - A test set with rich attributes and a time-constrained evaluation protocol

The Replay-Mobile Database for face spoofing consists of 1190 video clips of photo and video attack attempts to 40 clients, under different lighting conditions. These videos were recorded with current devices from the market -- an iPad Mini2 (running iOS) and a LG-G4 smartphone (running Android). This Database was produced at the Idiap Research Institute (Switzerland) within the framework of collaboration with Galician Research and Development Center in Advanced Telecommunications - Gradiant (Spain). Source: Replay-Mobile Image Source: https://core.ac.uk/download/pdf/148024307.pdf

A renovation of Labeled Faces in the Wild (LFW), the de facto standard testbed for unconstraint face verification. Source: CALFW

A renovation of Labeled Faces in the Wild (LFW), the de facto standard testbed for unconstraint face verification. There are three motivations behind the construction of CPLFW benchmark as follows: 1.Establishing a relatively more difficult database to evaluate the performance of real world face verification so the effectiveness of several face verification methods can be fully justified. 2.Continuing the intensive research on LFW with more realistic consideration on pose intra-class variation and fostering the research on cross-pose face verification in unconstrained situation. The challenge of CPLFW emphasizes pose difference to further enlarge intra-class variance. Also, negative pairs are deliberately selected to avoid different gender or race. CPLFW considers both the large intra-class variance and the tiny inter-class variance simultaneously. 3.Maintaining the data size, the face verification protocol which provides a 'same/different' benchmark and the same identities in LFW, so one can easily apply CPLFW to evaluate the performance of face verification. Source: CPLFW

During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to face recognition. Traditional face recognition systems may not effectively recognize the masked faces, but removing the mask for authentication will increase the risk of virus infection. Inspired by the COVID-19 pandemic response, the widespread requirement that people wear protective face masks in public places has driven a need to understand how face recognition technology deals with occluded faces, often with just the periocular area and above visible. To cope with the challenge arising from wearing masks, it is crucial to improve the existing face recognition approaches. Recently, some commercial providers have announced the availability of face recognition algorithms capable of handling face masks, and an increasing number of research publications have surfaced on the topic of face recognition on people wearing masks. However, due to the sudden outbreak of the epidemic, there is yet no publicly available masked face recognition benchmark. In this workshop, we will organise Masked Face Recognition (MFR) challenge and focus on bench-marking deep face recognition methods under the existence of facial masks. In this challenge, we will evaluate the accuracy of following testsets: Accuracy between masked and non-masked faces. Accuracy among children(2~16 years old). Accuracy of globalised multi-racial benchmarks. We ensure that there's no overlap between these testsets and public available training datasets, as they are not collected from online celebrities. The globalised multi-racial testset contains 242,143 identities and 1,624,305 images. Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images. There are totally 13,928 positive pairs and 96,983,824 negative pairs. Children testset contains 14,344 identities and 157,280 images. There are totally 1,773,428 positive pairs and 24,735,067,692 negative pairs. For Mask set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4). For Children set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.0001(e-4). For other sets, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). Participants are ordered in terms of highest scores across two datasets: TAR@Mask and TAR@MR-All, by the formula of 0.25 * TAR@Mask + 0.75 * TAR@MR-All.

An evaluation protocol for face verification focusing on a large intra-pair image quality difference. Real-world face recognition applications often deal with suboptimal image quality or resolution due to different capturing conditions such as various subject-to-camera distances, poor camera settings, or motion blur. This characteristic has an unignorable effect on performance. Recent cross-resolution face recognition approaches used simple, arbitrary, and unrealistic down- and up-scaling techniques to measure robustness against real-world edge-cases in image quality. Thus, we propose a new standardized benchmark dataset and evaluation protocol derived from the famous Labeled Faces in the Wild (LFW). In contrast to previous derivatives, which focus on pose, age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in the Wild (XQLFW) maximizes the quality difference. It contains only more realistic synthetically degraded images when necessary. Our proposed dataset is then used to further investigate the influence of image quality on several state-of-the-art approaches. With XQLFW, we show that these models perform differently in cross-quality cases, and hence, the generalizing capability is not accurately predicted by their performance on LFW. Additionally, we report baseline accuracy with recent deep learning models explicitly trained for cross-resolution applications and evaluate the susceptibility to image quality.

The IDiff-Face dataset was proposed in the paper "IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models". This dataset is synthetically generated using the IDiff-Face model.

A new face annotation dataset with balanced distribution between genders and ethnic origins. Source: SensitiveNets: Learning Agnostic Representations with Application to Face Images

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

Although deep face recognition has achieved impressive results in recent years, there is increasing controversy regarding racial and gender bias of the models, questioning their trustworthiness and deployment into sensitive scenarios. DemogPairs is a validation set with 10.8K facial images and 58.3M identity verification pairs, distributed in demographically-balanced folds of Asian, Black and White females and males. We also propose a benchmark of experiments using DemogPairs over state-of-the-art deep face recognition models in order to analyze their cross-demographic behavior and potential demographic biases (see figure below).

QMUL-SurvFace is a surveillance face recognition benchmark that contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes over wide space and time. Source: Surveillance Face Recognition Challenge

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

MeGlass is an eyeglass dataset originally designed for eyeglass face recognition evaluation. All the face images are selected and cleaned from MegaFace. Each identity has at least two face images with eyeglass and two face images without eyeglass. It contains 47,817 images from 1,710 different identities. Source: https://github.com/cleardusk/MeGlass Image Source: https://github.com/cleardusk/MeGlass

Contains over 11000 images of 1000 identities with different types of disguise accessories. The dataset is collected from the Internet, resulting in unconstrained face images similar to real world settings. Source: Recognizing Disguised Faces in the Wild

Drone Surveillance of Faces, is a large-scale drone dataset intended to facilitate research for face recognition using drones. Data has been captured at two outdoor locations: (i) at the ground level, where subjects are asked to walk in a park-like environment, and (ii) at the terrace of a building. For each location and surveillance scenario, data is captured twice: (i) during the morning and (ii) during the evening, before sunset. For each combination of location, surveillance use case, and time of capture, there exist 25 videos featuring 58 subjects. Each subject belongs to the age bracket of (18, 40) years, and each video contains subjects appearing in groups of 2-3. For a particular setting combination, one group appears in only one video. Since there exist eight combinations of location, surveillance scenario, and time of capture, each group of subjects occur in eight videos, thus resulting in a total of 200 videos. The subjects and pairings remain consistent across different settings. The directory structure represents the combination of location, use case and time of capture.

iQIYI-VID dataset, which comprises video clips from iQIYI variety shows, films, and television dramas. The whole dataset contains 500,000 videos clips of 5,000 celebrities. The length of each video is 1~30 seconds. Source: iQIYI-VID