I am currently using the available OpenVINO model for detecting a face. Training a model meant doing it from scratch for detecting a face both on RGB and interpolate the results to determine if it is real?
Ssky
- Aug 31, 2021
- Joined Aug 20, 2021
- 0 best answers
Yes, getting the head size is insufficient, and will printing an A4 head bending to the face is a possible attack.
The eyes closer than nose method I didn't continue as it is possible to detect a face that is slightly tilted to the camera (e.g. left eye/spectacle is closer than nose to the camera). Maybe I should treat that as a fake or hint person to face camera.The binning one I probably will try as not sure what to do with the depth so far.
I guess there will be some dirty coding (mixing methods) to achieve what I want. Thanks,
Not sure if OpenPilot is known widely. It isn't focused on bicycle detection but is a working legit product that let your car self-drive safely.
https://github.com/commaai/openpilot
https://comma.ai/I have a use case to do a liveness check (e.g. detect fake/static faces on camera) and was wondering if Oak-D can do it making use of the depth information?
I am not an expert but tried and failed
- using the depth frame after detecting the face (eg try to see if depth of pixels near centroid are changing and not flat like a screen but the depth seem to flicker regardless)
- estimate the height of subject (I tried some formula but inaccurate, #height=detection.spatialCoordinates.z/(pixel height)/focal length (4.52mm?)). It will filter small faces in camera but a actual size printed face would defeat this I guess.
I read about getting 3d position of face landmark and am not sure if anyone tried and got one to share (though I think a printed face that is contour to a person's face will defeat it again)?
Thanks
Is there someone who had a similar use case and found success?