The Replay-Attack Database for face spoofing consists of 1300 video clips of photo and video attack attempts to 50 clients, under different lighting conditions. All videos are generated by either having a (real) client trying to access a laptop through a built-in webcam or by displaying a photo or a video recording of the same client for at least 9 seconds.
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The MSU-MFSD dataset contains 280 video recordings of genuine and attack faces. 35 individuals have participated in the development of this database with a total of 280 videos. Two kinds of cameras with different resolutions (720×480 and 640×480) were used to record the videos from the 35 individuals. For the real accesses, each individual has two video recordings captured with the Laptop cameras and Android, respectively. For the video attacks, two types of cameras, the iPhone and Canon cameras were used to capture high definition videos on each of the subject. The videos taken with Canon camera were then replayed on iPad Air screen to generate the HD replay attacks while the videos recorded by the iPhone mobile were replayed itself to generate the mobile replay attacks. Photo attacks were produced by printing the 35 subjects’ photos on A3 papers using HP colour printer. The recording videos with respect to the 35 individuals were divided into training (15 subjects with 120 videos) an
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CASIA-MFSD is a dataset for face anti-spoofing. It contains 50 subjects, and 12 videos for each subject under different resolutions and light conditions. Three different spoof attacks are designed: replay, warp print and cut print attacks. The database contains 600 video recordings, in which 240 videos of 20 subjects are used for training and 360 videos of 30 subjects for testing.
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SiW provides live and spoof videos from 165 subjects. For each subject, we have 8 live and up to 20 spoof videos, in total 4,478 videos. All videos are in 30 fps, about 15 second length, and 1080P HD resolution. The live videos are collected in four sessions with variations of distance, pose, illumination and expression. The spoof videos are collected with several attacks such as printed paper and replay.
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CASIA-FASD is a small face anti-spoofing dataset containing 50 subjects.
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CelebA-Spoof is a large-scale face anti-spoofing dataset with the following properties:
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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).
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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).
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HiFiMask is a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask). It contains a total amount of 54,600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors.
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The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database consists of 2904 short multi-modal video recordings of both bona-fide and presentation attacks. There are 555 bonafide presentations from 51 participants and the remaining 2349 are presentation attacks. The data is recorded from several channels including color, depth, thermal, infrared (spectra), and short-wave infrared (spectra).
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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.
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A multimodal database for eye blink detection and attention level estimation.
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The MLFP dataset consists of face presentation attacks captured with seven 3D latex masks and three 2D print attacks. The dataset contains videos captured from color, thermal and infrared channels.
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CelebA-Spoof is a large-scale face anti-spoofing dataset recently introduced in [53]. The dataset contains 625,537 images of 10,177 celebrities captured under different spoof mediums, environments and illumination conditions. The original dataset proposes three different evaluation protocols. For our experimentation, we focus on the most general ”intra” protocol, in which different spoof types, environments and illumination conditions are used for both training and testing.
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SiW (Spoofing in the Wild) is a face anti-spoofing dataset recently introduced in [29] where images are extracted from short videos captured at high resolution and 30 frames per second. In total, 4,478 videos are collected from 165 subjects including variations in spoof type, recording device, illumination condition, pose and facial expression.
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.
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SuHiFiMask (Surveillance High-Fidelity Mask) extends FAS to real surveillance scenes rather than mimicking low-resolution images and surveillance environments. It contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras.