Utility functions as aggregation functions in face recognition

Face recognition by computers in recent years has been a topic of intensive studies. In this problem, we witness several challenges: one has to cope with large data sets, solve problems of data extraction, and deal with poor quality of images caused by e.g., poor lighting of the subject. There have been a lot of algorithms and classifiers developed, which are aimed at recognizing faces of individuals. In this paper, we present a novel classification method, which involves a collection of classifiers with a certain utility function regarded as an aggregation operator. The nearest neighbor method with various similarity measures is used as a generic classifier for selected face areas. The main task is to assign photos of a person to one of the classes of image present in the available database. This problem is similar to the decision-making process with some evident analogies. If in face recognition, a single classifier is being used, the problem becomes similar to the one of decision-making with a single criterion. When having several classifiers, the problem resembles a problem of a multi-criteria decision making. The second scenario requires an aggregation of the results produced by different classifiers. The paper presents the use of the utility function which is well-known in the decision-making theory as an aggregation operator applied to the results of various classifiers. The study is focused on the two-factor utility function and its variants.