Hi there,

I'm developing a face detector. Now I found a problem is that, the individual  
trained  face detector works well on some evaluation dataset (AT&T face 
database & MIT non-face dataset) in terms of false postives (less than 1%) 
and false negatives (from 10%-1%). But when I apply some individual face 
detector on a set real-world images, the face detection rates vary from 
around 65% to 89%. 

In training I resize the AT&T face (a part of it, randomly selected 240 
patterns) to 22 by 20 and use all 440 pixels for classifier training. Is 
there some way to improve the performance of the face detector, or to get a 
robust face dtector? What I could think of is that dimension reduction might 
be useful, or to increase the size of training set. Could anyone please give 
me some general suggestion? Many thanks.


Bests,

Q.H.Wang