peak in false positives for detectMultiscale
I am doing a little research into the optimum detectMultiScale parameters to use for a face identification project and have plotted the number of false Positives against scale factor for min Neighbors 3,4,5,6, across minimum feature sizes 20:20, 30:30, and 40:40. See the attached graph.
I am using lbpcascade_frontalface.xml as the cascade classifier which was trained with a height and width of 24x24.
And I am using greyscale images resized to 200x200.
My question is why is there a an obvious and large peak in the number of false Positives at aroundscale Factor 1.45 (1.35 to 1.65) for each plot?
Thanks in anticipation of your help.
Langdale
that range of scale factors spans values that I would not recommend because they're too large and may miss detections (false negative). as for why you get false positives... no idea. try a denser and uniform sampling of scale factors. I would guess that the values near 1.05 and 1.45 are proper responses and the valleys beside those are how the algorithm fails catastrophically because it entirely misses the scales at which there are faces in your test images.