Example Based Learinig for View-Based Human Face Detection 6

Finding human faces automatically in an image is a difficult yet important first step to a fully automatic face recognition system. It is also an interesting academic problem because a successful face detection system can provide valuable insight on how one might approach other similar object and pattern detection problems. 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. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines, based on the difference feature vector, whether or not a human face exists at the current image location. We show empirically that the prototypes we choose for our distribution-based model, and the distance metric we adopt for computing difference feature vectors, are both critical for the success of our