Human Face Image Recognition: An Evidence Aggregation Approach

In this paper a novel analytically-based face recognition system is presented which allows incorporation of the importance of individual facial components in the recognition task. An image gallery of 40 people was used and the images searched to locate the face area and the head boundary. In this system the eyes are detected using learning graph templates, the mouth is detected using deformable templates, and the location of the nose is found by using integral projections based on the mouth and eye locations. Using a 3D model of a head, the facial rotations are estimated in order for the system to compensate for the rotation. The effect of the facial convexity is examined by using an overall recognition index, and an optimum value is used for the rest of the experiments. Each facial feature provides evidence for a classifier, with varying degrees of reliability. Furthermore, a fuzzy information fusion technique is applied to combine the decisions of individual classifiers with all possible combinations of classifiers. The reliability of each classifier is evaluated by an expert using fuzzy density measures in a training phase. An overall classification is derived using a fuzzy evidence aggregation method. The performance of the system is evaluated for various degrees of facial rotation using a cumulative score. The cumulative score provides the normal recognition rate as well as the rank of the next best matches.