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, Deepfake...
Next video shows a stress test on our implementation using different type of 2D/3D attacks:
The next video shows the result on a Youtube video from https://www.youtube.com/watch?v=2tgqX5WVhr0 to detect deepfakes:
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/
Cloud-based implementation: https://www.doubango.org/webapps/face-liveness/
Open source Computer Vision library: https://github.com/DoubangoTelecom/compv
- C++ API
- Supported attacks
- Identity concealment
- Deepfake detection
- Testing the competition
- ISO ISO/IEC 30107
- Why liveness detector is required for facial recognition systems?
- Passive versus Active liveness detection
- Improving the recall score
- Integration with your existing application
- Error and success codes
- Memory management design
- Muti-threading design
- Known issues