title: Viola Jones face detection - variations in object/face size
body: <p>I'm trying to understand Viola Jones method, and I've mostly got it. </p>
  
  <p>It uses simple Haar like features boosted into strong classifiers and organized into layers /cascade in order to accomplish better performances (not bother with obvious 'non object' regions).</p>
  
  <p>I think I understand integral image and I understand how are computed values for the features. </p>
  
  <p>The only thing I can't figure out is how is algorithm dealing with the face size variations.</p>
  
  <p>As far as I know they use 24x24 subwindow that slides over the image, and within it algorithm goes through classifiers and tries to figure out is there a face/object on it, or not. </p>
  
  <p>And my question is - what if one face is 10x10 size, and other 100x100? What happens then?</p>
  
  <p>And I'm dying to know what are these first two features (in first layer of the cascade), how do they look like (keeping in mind that these two features, acording to Viola&amp;Jones, will almost never miss a face, and will eliminate 60% of the incorrect ones) ? How??</p>
  
  <p>And, how is possible to construct these features to work with these statistics for different face sizes in image? </p>
  
  <p>Am I missing something, or maybe I've figured it all wrong?</p>
  
  <p>If I'm not clear enough, I'll try to explain better my confusion. </p>