Face Verification

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 Cohn-Kanade (CK+) dataset contains 593 video sequences from a total of 123 different subjects, ranging from 18 to 50 years of age with a variety of genders and heritage. Each video shows a facial shift from the neutral expression to a targeted peak expression, recorded at 30 frames per second (FPS) with a resolution of either 640x490 or 640x480 pixels. Out of these videos, 327 are labelled with one of seven expression classes: anger, contempt, disgust, fear, happiness, sadness, and surprise. The CK+ database is widely regarded as the most extensively used laboratory-controlled facial expression classification database available, and is used in the majority of facial expression classification methods. Source: EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-time Facial Expression Recognition

MegaFace was a publicly available dataset which is used for evaluating the performance of face recognition algorithms with up to a million distractors (i.e., up to a million people who are not in the test set). MegaFace contains 1M images from 690K individuals with unconstrained pose, expression, lighting, and exposure. MegaFace captures many different subjects rather than many images of a small number of subjects. The gallery set of MegaFace is collected from a subset of Flickr. The probe set of MegaFace used in the challenge consists of two databases; Facescrub and FGNet. FGNet contains 975 images of 82 individuals, each with several images spanning ages from 0 to 69. Facescrub dataset contains more than 100K face images of 530 people. The MegaFace challenge evaluates performance of face recognition algorithms by increasing the numbers of “distractors” (going from 10 to 1M) in the gallery set. In order to evaluate the face recognition algorithms fairly, MegaFace challenge has two protocols including large or small training sets. If a training set has more than 0.5M images and 20K subjects, it is considered as large. Otherwise, it is considered as small. NOTE: This dataset has been retired. Source: A Deep Face Identification Network Enhanced by Facial Attributes Prediction

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 IARPA Janus Benchmark A (IJB-A) database is developed with the aim to augment more challenges to the face recognition task by collecting facial images with a wide variations in pose, illumination, expression, resolution and occlusion. IJB-A is constructed by collecting 5,712 images and 2,085 videos from 500 identities, with an average of 11.4 images and 4.2 videos per identity. Source: von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification Image Source: Ruan et al

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

The Oulu-CASIA NIR&VIS facial expression database consists of six expressions (surprise, happiness, sadness, anger, fear and disgust) from 80 people between 23 and 58 years old. 73.8% of the subjects are males. The subjects were asked to sit on a chair in the observation room in a way that he/ she is in front of camera. Camera-face distance is about 60 cm. Subjects were asked to make a facial expression according to an expression example shown in picture sequences. The imaging hardware works at the rate of 25 frames per second and the image resolution is 320 × 240 pixels. Source: Facial expression recognition from near-infrared videos Image Source: https://arxiv.org/abs/1712.03474

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

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.

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

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.

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

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

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.

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.

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.

Large Age-Gap (LAG) is a dataset for face verification, The dataset contains 3,828 images of 1,010 celebrities. For each identity at least one child/young image and one adult/old image are present.

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).

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 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.