 The goal of this ongoing project is to formulate
paradigms for detection and recognition of human faces
in complex backgrounds. One of the applications would
be towards adding face oriented queries to our image
database project. For instance, "Find all the images with 3
or more faces." With recognition capabilities, we could
then augment the queries to identity oriented retrieval.
For example, "Find all the images which contain the
Queen of England."
The fundamental principle which we are exploiting for
our face detector is the Kullback measure of relative
information. This measure has the properties that bounds
on the classification error probabilities can be proven,
and that it leads to feature classes which are better for
classification. We apply it toward finding the most
informative pixels (MIP), which from a pattern
recognition perspective should maximize the class
separation. Since the class probabilities are dependent on
their neighbors, we model the image as a Markov random
field (MRF) and calculate the MIP using a first order
assumption. |
