Edited by author 10-25-2001 07:09 AM
In this paper, cascading works as follow: if a less-powerful classifier says a patch is not a face, it is almost certainly not a face. However if it says it is a face, it could be a false positive. A more powerful classifier need to be used. Can you train the network to do the opposite? Train a first-stage classifier to have low false-positive, but if it says a patch is not a face, pass it on to higher classifier to decide if it is a false-negative? Seems to be ok for me, but maybe I am mising something. Is this a dumb question? --- wait! Ok it's a dumb question, after reading Ian's comment. Doh!

The second question is: Ok this model is cool. It selects the most important features from a huge set of features. But maybe that's because the huge set of features is largely junk features to begin with? What happends if you start with a (large) set of good features (steerable)? Will the AdaBoost procedure still be useful? Also, the greediness of AdaBoost might have a problem in the following situation: it's possible that the features are not independent to each other. That is, feature A might make no sense, unless feature B is also included. It seems to me that AdaBoost will not be smart enough for this siatuation.