State-of-the-art face recognition models are trained on millions of real human face images collected from the internet. DigiFace-1M aims to tackle three major problems associated with such large-scale face recognition datasets.
We build on the synthetic face generation framework of Wood et al. to create a dataset of over one million synthetic face images. This synthetics dataset helps us to overcome three primary shortcomings of existing large-scale face recognition datasets:
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We define identity as a unique combination of facial geometry, texture, eye color and hair style. For each identity, we sample a set of accessories including clothing, make-up, glasses, face-wear and head-wear.
While hair style can change for an individual, most people maintain similar hair style (for both facial and head hair) which makes hair style an important cue for the person's identity. Therefore, for the same identity, we randomize only the color, density and thickness of the hair (top row) and avoiding the impression of changing identity (bottom row). This simulates aging to some extent as hair typically becomes grayer, sparser and thinner during aging. The hair style is only changed when the added head-wear is not compatible with the original hair style.
SynFace is the current state-of-the-art for face recognition model trained on synthetic faces. They used DiscoFaceGAN to generate 500K synthetic faces of 10K unique identities. We significantly outperform SynFace across all datasets, suggesting that our rendered synthetic faces are better than GAN-generated faces for learning face recognition. This is likely because GAN-generated images do not enforce identity or geometric consistency, and are not effective at changing accessories. The GAN models also have unresolved ethical and bias concerns as they are typically trained on large-scale real face datasets.
When a small number of real face images are available, we can use them to fine-tune the network that is pre-trained on our synthetic data. Such fine-tuning significantly improves the accuracy across all datasets.
However, there remains a substantial accuracy gap from the state-of-the-art methods that are trained on large-scale real face datasets. This gap can be reduced by adopting better data augmentation or by improving the realism of the face generation pipeline. We leave this as future work.
Flickr-Faces-HQ Dataset (FFHQ) is a dataset consisting of human faces and includes datasets categorized by age, ethnicity, and image background. It also has great coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled from Flickr and then automatically aligned and cropped. It consists of 70,000 high-quality PNG images at 10241024 resolution.
Tufts Face Dataset is a comprehensive, large-scale face dataset that contains 7 image modalities: visible, near-infrared, thermal, computerized sketch, LYTRO, recorded video, and 3D images. The dataset contains over 10,000 images, where 74 females and 38 males from more than 15 countries with an age range between 4 to 70 years old are included.
This is a helpful dataset to benchmark facial recognition algorithms for sketches, thermal, NIR, 3D face recognition, and heterogamous face recognition.
Labeled Faces in the Wild (LFW) Dataset is a database of face photographs designed for studying the problem of unconstrained face recognition. Labeled Faces in the Wild is a public benchmark for face verification, also known as pair matching. The dataset is 173MB and it consists of over 13,000 images of faces collected from the web.
UTKFace dataset is a large-scale face dataset with a long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variations in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc.
This dataset can be used as a building block to track faces in images and video, analyze facial expressions, detect dysmorphic facial signs for medical diagnosis, and biometrics or facial recognition.
This dataset is a large-scale facial expression dataset that consists of face image triplets along with human annotations that specify, which two faces in each triplet form the most similar pair in terms of facial expression. This dataset is 200MB, which includes 500K triplets and 156K face images. The aim of this dataset is to aid researchers working on topics related to facial expression analysis such as expression-based image retrieval, expression-based photo album summarization, emotion classification, expression synthesis, etc.
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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.
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.
The Extended Yale B database contains 2414 frontal-face images with size 192168 over 38 subjects and about 64 images per subject. The images were captured under different lighting conditions and various facial expressions.
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.
The Adience dataset, published in 2014, contains 26,580 photos across 2,284 subjects with a binary gender label and one label from eight different age groups, partitioned into five splits. The key principle of the data set is to capture the images as close to real world conditions as possible, including all variations in appearance, pose, lighting condition and image quality, to name a few.
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.
The color FERET database is a dataset for face recognition. It contains 11,338 color images of size 512768 pixels captured in a semi-controlled environment with 13 different poses from 994 subjects.
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 2016 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.
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.
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).
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.
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. 152ee80cbc