Multi-objective Evolutionary Approach for Biometric Fusion

Abstract

In recent years, a noticeable amount of research has been focused on biometric fusion. A new area is looking at utilization of AdaBoost-type learning methods in biometric fusion domain. These methods rely on an idea that by selecting a variety of biometric classifiers the error rate can be reduced. This paper presents a new evolutionary algorithm based on the multi-objective genetic approach, which automatically preserves diversity in face detection system. The proposed algorithm creates classifiers based on the amount of error computed for each class, and then uses multi-objective genetic algorithm to combine them to produce a set of powerful ensembles. The application is developed for face detection biometric system.