Learning Human Face Detection in Cluttered Scenes

This paper presents an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based “face” and “non-face” prototype clusters. A 2-Value metric is proposed for computing distance features between test patterns and the distribution-based face model during classification. We show empirically that the prototypes we choose for our distribution-based model, and the metric we adopt for computing distance feature vectors, are both critical for the success of our system.