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https://github.com/doubangotelecom/facelivenessdetection-sdk

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...).
https://github.com/doubangotelecom/facelivenessdetection-sdk

ai aml biometrics deep-learning deepfake deepfake-detection ekyc face-detection face-recognition keras kyc liveness liveness-detection liveness-probe machine-learning spoofing spoofing-attack tensorflow

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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...).

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README 

        
- [Getting started](#getting-started)

  - [Checking out the source code](#checkout-source)

  - [Trying samples](#trying-samples) (**C++**, **C#**, **Java** and **Python**)

  - [Getting help](#technical-questions)



- Online web demo at https://www.doubango.org/webapps/face-liveness/

- Full documentation for the SDK at https://www.doubango.org/SDKs/face-liveness/docs/

- Supported languages (API): **C++**, **C#**, **Java** and **Python**

- Open source Computer Vision Library: https://github.com/DoubangoTelecom/compv




To our knowledge we're **the only company in the world** that can perform 3D liveness check and [identity concealment](https://www.doubango.org/SDKs/face-liveness/docs/Identity_concealment.html) detection from a single 2D image. We outperform [the competition](https://www.doubango.org/SDKs/face-liveness/docs/Testing_the_competition.html) ([FaceTEC](https://www.doubango.org/SDKs/face-liveness/docs/Testing_the_competition.html#facetec), [BioID](https://www.doubango.org/SDKs/face-liveness/docs/Testing_the_competition.html#bioid), [Onfido](https://www.doubango.org/SDKs/face-liveness/docs/Testing_the_competition.html#onfido), [Huawei](https://www.doubango.org/SDKs/face-liveness/docs/Testing_the_competition.html#huawei)...) in speed and accuracy. **Our implementation is Passive/Frictionless and only takes few milliseconds.**



[Identity concealment](https://www.doubango.org/SDKs/face-liveness/docs/Identity_concealment.html) 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](https://doubango.org/videos/liveness/stress-doubango.jpg)](https://doubango.org/videos/liveness/stress-doubango-x264.mp4) | [![Doubango AI: Deepfake detection](https://doubango.org/videos/liveness/deepfake-zelinsky.jpg)](https://doubango.org/videos/liveness/deepfake-zelinsky-x264.mp4) |






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](assets/FaceLivenessDetection-Models) are hosted on private repository for obvious reasons. You have to [send us a mail](https://www.doubango.org/#contact) 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](samples) folder and choose your prefered language.





# Technical questions #

Please check our [discussion group](https://groups.google.com/forum/#!forum/doubango-ai) or [twitter account](https://twitter.com/doubangotelecom?lang=en)