An efficient classifier fusion for face recognition including varying illumination

Abstract

In this paper, we propose an efficient classifier fusion for face recognition under varying illumination environments by taking classifier fusion's advantage of environment context identification. Adaptation to dynamically changing environments is very important since advanced applications become pervasive and ubiquitous. The proposed classifier fusion system, called BCF (Bayesian based classifier fusion), adopts the concept of face context awareness and evolutionary computing. But aside the difference of classifiers the training data performs main role in them consequently the results from the classifiers couldn't be so individual from each other to make decision by considering them. The system working environments are clustered and identified as face environmental context. Majority voting (MV), Maximum based fusion (MX) and Minimum based fusion (MN) are used to explore the most effective action configuration for each identified context. Once the context knowledge is constructed, the system can adapt to varying environment in real-time. The superiority of the proposed scheme is shown using three face image data sets: Inha, FERET, and Yale. © Springer-Verlag Berlin Heidelberg 2005.