Databases for Face Antispoofing and Recognition
Face databases play a major role in antispoofing as they help train and test facial liveness detection, as well as facial recognition systems.
Face Antispoofing Databases: Types, Role & Application Areas
A face antispoofing database represents a collection of facial images used for training and testing existing detection solutions. These datasets range in sample variety, visual quality, digital face manipulation methods applied to them, types of Presentation Attacks (PAs) featured, environments, modality and sensors, etc. The earliest databases of this kind can be traced back to 1993 when the FERET database was released by NIST together with the Army Research Laboratory. The database was created to make facial recognition algorithms suitable for commercial usage.
The emergence of deepfakes and their growing concern emphasized the need for diverse datasets to train antispoofing detection methods. Therefore, around 2018, more datasets were introduced that offer a bigger selection of samples, individuals, scenery, and manipulation techniques. One of the largest collections is the DFDC dataset, which accompanied the eponymous challenge: it contains 128,154 videos with paid actors.
Today’s databases extend beyond mere face detection. They feature various modalities, such as Near Infrared (NIR) and Shortwave Infrared (SWIR) sensors, novel attack types, controlled and open field environments, indoor and outdoor scenery, different lighting levels, and other nuances that help train a more accurate facial recognition/antispoofing system.
Main Databases for Face Antispoofing
Since the early 1990s a number of face recognition (FR) databases have been developed. The following examples can be highlighted:
The first FR database in history, Face Recognition Technology (FERET) was conceived in December 1993. Over the course of three years, 14,126 images of 1,199 individuals were collected with 365 duplicates taken on different days. In 2003, the images were upgraded to 24-bit HD quality. Despite its age, the database is still available on demand.
Face Recognition Grand Challenge (FRGC) organized by NIST, began in 2002 and lasted for one academic year. With the goal "to promote and advance face recognition technology" for the US government, it also introduced a database of the same name. It includes 50,000 samples collected at the University of Notre Dame during the subject sessions. In other words, images of a person were captured at the same time when their biometric data was obtained.
The sample selection includes 4 controlled studio-quality still images, 2 uncontrolled still images, and 1 three-dimensional image. They contain a limited variety in terms of ethnicity and lighting, as well as balanced gender diversification. Minolta Vivid sensor was used for 3D images, while Canon PowerShot G2 was used for taking still images.
CelebA-Spoof is an extensive non-commercial dataset, which contains 625,537 photos borrowed from 10,177 individuals. The sample material features 43 rich attributes separated in 4 categories: Spoof Type, Illumination Condition, and Environment.
The CelebA-Spoof database includes a variety of illumination, types of fake imagery, scenery, and so on. To make it more effective, a mixture of facial contours, hair, complexion, skin tones, expressions, and other accessories is also provided.
MorphDB is a database developed for detecting morphed images, specifically those used in paper documents. It offers 100 morph images produced with the Sqirlz Morph 2.1 application with the morphing factor in the [0.3;0.4] range. Images are presented as both digital and printed copies that were later re-scanned. Like many other morph datasets, MorphDB seems to be publicly unavailable.
AMSL is a publicly accessible morph dataset based on the Face Research Lab London Set. It consists of 2,175 facial morphs that are in compliance with the ICAO portrait quality guidelines and can be stored on an electronic Machine-Readable Travel Document (eMRTD) chip. (As face morphs are mostly used for spoofing border control systems).
Real Face Databases
A number of databases have a specific purpose of delivering ‘real-life’ images that were not staged in a controlled environment like a professional studio. A significant advantage of such material would be its numerous imperfections such as insufficient lighting, unpredictable angles, blurring, lower image definition, random expressions, and so on.
Among them researchers highlight:
Labelled Faces in the Wild
Labelled faces in the Wild was conceived at the University of Massachusetts and features data corpus obtained from the videos and images available online. The database includes highly imperfect images captured in various environments. It impacts quality, illumination, and other parameters. There are 13,233 images in total, featuring 5,749 people. However, the labelling accuracy of the used images is only 77%, which makes training and evaluating somewhat challenging.
The BioID Face Database
The BioID dataset incorporates 1,521 gray level images with a 384 x 286 pixel resolution. The dataset mostly focuses on face detection rather than recognition or antispoofing. It features an assortment of images photographed in office or home environments, with different poses, expressions, and illumination levels.
Caltech 10000 Web Faces
Caltech 10,000 Web Faces provides a repertoire of samples captured in real-life conditions. The database provides a mixture of poses, ages, illumination and facial expressions. It provides added diversity in the form of varied background details. However, it does not give specific identities of the people presented, which can hinder facial recognition training.
Landmarked databases pinpoint coordinates of the facial features — eyes, nose tip, lips — which is necessary for certain software applications.
Milborrow University of Cape Town (MUCT) dataset contains faces of 755 volunteers with 76 landmarks manually highlighted. The database provides ethnic, age and gender diversity with random expressions, face occlusions, poses, and so on. The images included are captured with five cameras simultaneously and present an image quality of 640 x 480 pixels.
Created at Purdue University, it contains 508 color images (768 x 576 resolution), featuring 126 individuals. Additionally, 22 landmarks are pinpointed in the featured samples with a variety of emotional expressions such as neutral, mild anger, and smile.
The IMM dataset from the Technical University of Denmark features 240 images (640 x 480 resolution) of 40 people. 58 landmarks are highlighted with six different poses and varying light conditions.
Compiled at the Poznan University of Technology, the PUT dataset contains 2,193 color photos (2048 x 1536 resolution) of 200 individuals with 199 landmarks highlighted.
The XM2VTS database is a freely available dataset, which offers a landmark subset. The subset features 2,360 color photos (720 x 576 resolution) of 295 people with 68 landmarks highlighted. It is noted that this dataset is quite effective for training Active Shape Models (ASMs), as it offers a rich assortment of faces and facial landmarks.
AnimeCeleb is a large-scale database comprising of animated images. It was created from 3,613 3D cartoon models with full-body information — such as skeletal bones — borrowed from DeviantArt and Niconi Solid.
AnimeCeleb uses the obtained animations and turns them into 2D drawings with the open-source Blender software, with such aspects as Camera alignment, Light condition, and Image resolution. The dataset focuses on researching facial morphs, as well as on manipulating poses, head rotations, colorization, image harmonization, and other animation aspects.
In order to regulate the quality of databases created, certain standards are used. Specifically, ISO/IEC 19794-5:2011 and ISO/IEC 39794-5:2019, which refer to data and biometric data interchange. These standards specify parameters such as Unified quality score FaceQnet (JRC), Capture-related quality elements and Subject-related quality elements etc.
- DFDC (Deepfake Detection Challenge)
- NIR and SWIR Questions & Answers
- Color FERET dataset sample
- Color FERET Database
- Face Recognition Grand Challenge
- FRGC (Face Recognition Grand Challenge)
- Overview of the Face Recognition Grand Challenge
- CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations
- Sqirlz Morph 2.1
- AMSL Face Morph Image Data Set
- Face Research Lab London Set
- Portrait Quality (Reference Facial Images for MRTD)
- Example of an MRTD
- Labelled faces in the Wild
- Labelled Faces in the Wild sample
- The BioID dataset
- Caltech 10,000 Web Faces
- Caltech database samples
- The MUCT Landmarked Face Database
- Milborrow University of Cape Town (MUCT) dataset
- IMM dataset sample
- The IMM Face Database - An Annotated Dataset of 240 Face Images
- Repositioned PUT dataset sample
- XM2VTS sample collage
- The XM2VTS database
- Active Shape Models by Wikipedia
- AnimeCeleb: Large-Scale Animation CelebFaces Dataset via Controllable 3D Synthetic Models
- Blender software
- ISO/IEC 19794-5:2011
- ISO/IEC 39794-5:2019
- Biometric Data Interchange Standards and ICAO 9303 Relevance