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
This dataset is an OSN-transmitted (OSN = Online Social Network) version of the CASIA dataset. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.
This dataset is an OSN-transmitted (Online Social Network) version of the Columbia dataset. Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.
This database offers iris images (with and without contact lenses) of the same eyes captured shortly one after another with illumination coming from two different locations. 5,796 iris images in total were acquired by the LG IrisAccess 4000 sensor from 119 subjects. This set is divided into four subsets used in the experiments: (a) 1,800 images of irises wearing regular (with dot-like pattern) textured contact lenses, as shown in Fig. 6a in the wAcv 2019 paper; (b) 864 images of irises wearing irregular (without dot-like pattern) textured contact lenses, as shown in Fig. 6b in the WACV 2019 paper; (c) 1,728 images of irises wearing clear contact lenses (without any visible pattern), and (d) 1,404 images of authentic irises without any contact.
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
