Learning a distribution-based face model for human face detection

Abstract

We present a distribution-based modeling cum example-based learning approach for detecting human faces in cluttered scenes. The distribution-based model captures complex variations in human face patterns that cannot be adequately described by classical pictorial template-based matching techniques or geometric model-based pattern recognition schemes. We also show how explicitly modeling the distribution of certain "facelike" nonface patterns can help improve classification results.