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DATASET
The Dataset for successfully obtaining Level 2 iBeta certificate in "Presentation Attack Detection"
Liveness Detection
for iBeta level 2
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Liveness Detection
Machine Learning
iBeta
Computer Vision
Ability of a machine to interpret, analyze, and understand visual data
The ability to distinguish whether biometric data is being captured from a live or fake source
Independent laboratory that certifies liveness detection systems
Enables computer systems to automatically learn from data and make predictions
> 33 000
video
100%
iBeta Level 2
107
GB
Attack
types:
Printed photos with outlines
Custom silicone mask
2D masks with cutouts
Printed 3D Masks
Latex masks
Consultation
Download
the PDF
Dataset for
iBeta
certification:
Created in accordance with laboratory requirements
Ready for use in training liveness detection systems
Proven to be effective in obtaining iBeta Level 2 certification
Demonstrates impeccable accuracy without any Type 1 and Type 2 errors
Extra
services
Bounding Box labeling and classification
Collecting real-video and attacks from devices of interest (attacks from MacBook, iPad and iPhone webcams)
Collecting photos with street and home lighting at different times of the day
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See other
datasets
«Presentation Attack Detection» Level 1
The Dataset for successfully obtaining the iBeta Lab certificate in "Presentation Attack Detection" Level 1
Anti-spoofing dataset
40,000 videos and selfies with unique people
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250 000 images, 4 types of mask worn, 28 000 unique faces
Selfies and video dataset
Each person took a selfie on a webcam, took a selfie on a mobile phone. In addition, people recorded video from the phone and from the webcam, on which they pronounced a given set of numbers
Selfies and ID Images
Dataset includes: 4083 sets, which includes 2 photos of a person from his documents and 13 selfies
Portrait and 26 Photos
The dataset consists of various people's photos. Each person provided one selfie portrait and 26 photos from everyday life
Semantic face segmentation
Semantic face segmentation is a crucial aspect of computer vision, where each pixel of an image containing a face is assigned a semantic label, such as "skin," "eye," or "nose"
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