The Wide Multi Channel Presentation Attack (WMCA) database consists of 1941 short video recordings of both bonafide and presentation attacks from 72 different identities. The data is recorded from several channels including color, depth, infra-red, and thermal. Additionally, the pulse reading data for bonafide recordings is also provided. Preprocessed images for some of the channels are also provided for part of the data used in the reference publication. The WMCA database is produced at Idiap within the framework of “IARPA BATL” and “H2020 TESLA” projects and it is intended for investigation of presentation attack detection (PAD) methods for face recognition systems.
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
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
The Oulu-NPU face presentation attack detection database consists of 4950 real access and attack videos. These videos were recorded using the front cameras of six mobile devices (Samsung Galaxy S6 edge, HTC Desire EYE, MEIZU X5, ASUS Zenfone Selfie, Sony XPERIA C5 Ultra Dual and OPPO N3) in three sessions with different illumination conditions and background scenes. The presentation attack types considered in the OULU-NPU database are print and video-replay. The 2D face artefacts were created using two printers and two display devices. The videos of the 55 subjects are divided into three subject-disjoint subsets for training, development and testing. Four test protocols are used to evaluate the generalization capability of face PAD methods across three covariates: unknown environmental conditions (namely illumination and background scene), acquisition devices and presentation attack instruments (PAI). Each of the four unambiguously defined evaluation protocols introduces at least one previously unseen condition to the test set, which enables a fair comparison on the generalization capabilities between new and existing approaches. Image Source: https://www.researchgate.net/profile/Neil-Robertson/publication/333834759/figure/fig5/AS:897964780302339@1591102895306/Samples-from-the-OULU-NPU-database-From-top-to-bottom-is-the-three-sessions-with_W640.jpg
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
