Face Recognition
Detect and identify faces in visual imagery
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.

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