Reputation: 81
Ive been exploring dlib's face detector over its python API. On most images in my data set it seems to perform slightly better than cv2 on most images so I kept playing around with it on multiple faces in picture scenarios.
Going through dlib's python examples it seems like it would be possible to train these images but I am wondering if anyone has a suggestion how to make sure that the two faces on the far left and right are detected out of the box?
This is he image that I am having trouble finding all 6 faces on (https://images2.onionstatic.com/onionstudios/6215/original/600.jpg)
Upvotes: 0
Views: 2571
Reputation: 2637
Dlib has a very precise face detector. But it works bad detecting not frontal (like far left) and/or occluded faces (like far right).
Seeta (https://github.com/seetaface/SeetaFaceEngine) works better with those. But it's less precise.
Also I tried retraining Dlib's face detector. And obtained much lower precise than DLIB and less recall than Seeta. So, re-training DLIB seems not perfect idea.
Upvotes: 2
Reputation: 75
In my experience, Dlib does not do very well out of the box with obscured and profile faces out of the box. I would recommend training Dlib with more data of this kind.
Upvotes: 0