▁stage ▁is ▁very ▁fast ▁( a ▁few ▁operations ), ▁we ▁have ▁elimin ated ▁ 6 0 % ▁of ▁rect angles ▁not ▁containing ▁a ▁face ▁very ▁quickly . ▁ ▁The ▁training ▁procedure ▁for ▁one ▁stage ▁is ▁therefore ▁to ▁have ▁many ▁weak ▁learn ers ▁( simple ▁pixel ▁difference ▁operators ), ▁train ▁them ▁as ▁a ▁group ▁( raise ▁their ▁weight ▁if ▁they ▁give ▁correct ▁result ), ▁but ▁be ▁mind ful ▁of ▁having ▁only ▁a ▁few ▁active ▁weak ▁learn ers ▁so ▁the ▁computation ▁time ▁remains ▁low . ▁ ▁The ▁first ▁det ector ▁of ▁Vi ola ▁& ▁Jones ▁had ▁ 3 8 ▁stages , ▁with ▁ 1 ▁feature ▁in ▁the ▁first ▁stage , ▁then ▁ 1 0 , ▁ 2 5 , ▁ 2 5 , ▁ 5 0 ▁in ▁the ▁next ▁five ▁stages , ▁for ▁a ▁total ▁of ▁ 6 0 0 0 ▁features . ▁The ▁first ▁stages ▁remove ▁unw anted ▁rect angles ▁rapidly ▁to ▁avoid ▁pay ing ▁the ▁computational ▁costs ▁of ▁the ▁next ▁stages , ▁so ▁that ▁computational ▁time ▁is ▁spent ▁analyz ing ▁deeply ▁the ▁part ▁of ▁the ▁image ▁that ▁have ▁a ▁high ▁probability ▁of ▁containing ▁the ▁object . ▁ ▁Cas cade ▁training ▁▁ ▁C asc ades ▁are ▁usually ▁done ▁through ▁cost - aware ▁A DA boost . ▁The ▁sens itivity ▁threshold ▁( 0 . 8 ▁in ▁our ▁example ) ▁can ▁be ▁adjust ed ▁so ▁that ▁there ▁is ▁close ▁to ▁ 1 0 0 % ▁true ▁posit ives ▁and ▁some ▁false ▁posit ives . ▁The ▁procedure ▁can ▁then ▁be ▁started ▁again ▁for ▁stage ▁ 2 ,