Human Face Recognition: A Minimal Evidence Approach

Abstract

A face recognition system is described which employs a fuzzy information fusion technique to increase the overall recognition rate. The face images are searched for locating head area and face boundary. The eyes, and mouth are detected using rigid and deformable templates. Assuming a 3D head model, the face rotations are estimated, which allows for compensating rotated facial features back to a front, upright view. Each facial feature forms a source of information for classification. Based on a correlation technique using eye-forehead, mouth and nose windows, three classifiers are established. The output of each classifier is taken as a partial evidence in classification. The importance of each source is measured using a fuzzy density measure and the final classification is achieved using a fuzzy evidence aggregation method. The performance of the system is evaluated using a combined match score.