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3D Passive Face Liveness Detection (Anti-Spoofing) & Deepfake detection. A single image is needed to compute liveness score. 99,67% accuracy on our dataset and perfect scores on multiple public datasets (NUAA, CASIA FASD, MSU...).

Home Page: https://www.doubango.org/webapps/face-liveness/

Batchfile 0.27% C++ 73.99% CMake 0.03% Shell 0.09% C# 9.02% SWIG 1.02% Java 10.47% Perl 1.80% Python 3.31%
ekyc kyc aml face-detection face-recognition liveness-probe liveness-detection spoofing-attack tensorflow keras biometrics ai deep-learning machine-learning spoofing liveness deepfake deepfake-detection

facelivenessdetection-sdk's Introduction


To our knowledge we're the only company in the world that can perform 3D liveness check and identity concealment detection from a single 2D image. We outperform the competition (FaceTEC, BioID, Onfido, Huawei...) in speed and accuracy. Our implementation is Passive/Frictionless and only takes few milliseconds.

Identity concealment detects when a user tries to partially hide his/her face (e.g. 3D realistic mask, dark glasses...) or alter the facial features (e.g. heavy makeup, fake nose, fake beard...) to impersonate another user.

A facial recognition system without liveness detector is just useless.

We can detect and block all known spoofing attacks: Paper Print, Screen, Video Replay, 3D (silicone, paper, tissue...) realistic face mask, 2D paper mask, Concealment...

3D Liveness detection Deepfake detection
Doubango AI: 3D Face liveness detector stress test Doubango AI: Deepfake detection

Our passive (frictionless) face liveness detector uses SOTA (State Of The Art) deep learning techniques and can be freely tested with your own images at https://www.doubango.org/webapps/face-liveness/


Getting started

This version supports both Windows and Linux x86_64.

Checking out the source code

The deep learning models are hosted on private repository for obvious reasons. You have to send us a mail with your company name and Github user name (to be added to the private repo). The mail must come from @YourCompanyName, mails from other domains (e.g. @Gmail) will be ignored. The terms of use do not allow you to decompile or reverse engineer the models.

git clone --recurse-submodules -j8 https://github.com/DoubangoTelecom/FaceLivenessDetection-SDK

If you already have the code and want to update to the latest version: git pull --recurse-submodules

Trying samples (C++, C#, Java and Python)

Go to the samples folder and choose your prefered language.

Technical questions

Please check our discussion group or twitter account

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