Having spent a day detecting facing using Haar classifier (for frontal face), here's what I've figured out that it fails to recognize/detect faces where:
- Head is slightly tilted to left or right, even with frontal shots
- Thick moustache or beard
- Non pale/white skin coloured faces (failed to detect several African faces, and few faces from Indian sub-continent)
- Extreme mongoloid features
- Faces that have even slight bit of shade, i.e. not a highly uniformly lit face
Have tried modifying the scaleFactor and minNeighbours, without success.
So, it essentially seems to boil down to able to detect a narrow range of faces in ideal photographs, including person being fair skinned, with upright faces (no head tilt), minimal facial hair, uniform lighting of the face etc.
Or, did I miss anything ? Any particular setting or step to update/insert ?
Is it documented anywhere as to what type of faces (positive IDs) were used to create the Haar Cascade XML that is bundled with OpenCV ?
OTOH, I found this excellent Q&A, but again, there are many other questions on SO indicating people have had little success in creating their own Haar Cascades (challenge seems to be in training correctly). Guidance on how to approach this would be very helpful.