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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 around scale Factor 1.45 for each plot?

Thanks in anticipation of your help.

Langdale

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 around scale Factor 1.45 for each plot?

Thanks in anticipation of your help.

Langdale

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 around scale aroundscale Factor 1.45 (1.35 to 1.65) for each plot?

Thanks in anticipation of your help.

Langdale