Beckman Institute for Advance Science and Technology Image Formation and Processing Group In this paper we present a visual learning approach that uses non-parametric probability estimators. We use entropy analysis over the training set in order to select the features that best represent the pattern class of faces, and set up discrete probability models. These models are tested in the context of maximum likelihood detection of faces. Excellent results are reported in terms of the correct-answer-false-alarm tradeoff as well as in terms of the computational requirements of the systems.