How Viola-Jones face detector work for multiple size of faces?

Question

I am implementing Viola-Jones face detector to detect faces in still images and it work preety for images having same size as of my training size. However I am not getting how the face detector work for multiple size faces?

If the training size of my images is 24*24 and if I want to detect faces in detector window of 30*30 how I need to rescale the haar-feature so that it will work for 30*30 size detector window working with the same threshold.

One more thing, do the position of Haar-feature also changes with different size detector window and if yes how?

Answer 1

Say you're representing a rectangle found inside a Haar wavelet with x, y, w and h variables where x and y represent to top left corner of the rectangle relative to the detector's top left boundaries, w its width and h its height. You can rescale the whole detector by a factor s each Haar wavelet rectangle with the following pseudo-code:

for all rectangle i in the Haar wavelet do
    tempRectangle = rectangle[i];
    tempRectangle.x = tempRectangle.x * s
    tempRectangle.y = tempRectangle.y * s
    tempRectangle.h = tempRectangle.h * s
    tempRectangle.w = tempRectangle.w * s

    //Read the pixels contained in tempRectangle region and
    //calculate this rectangle's contribution to the feature value
    //considering the respective weight of rectangle[i].
end for

So, let's assume that a single Haar-lke feature has the base size of 24x24 pixels. Such feature is composed of 2 rectangles r1=(10,15,8,4) and r2=(4, 8, 8, 4), where r=(x,y,w,h). When you rescale your detector by a factor s=1.25, this feature rectangles should become r1=(12.5, 18.75, 10, 5) and r2=(5, 10, 10, 5).