Developing a late fusion of multi facial components for facial recognition with a voting method and global weights

Abstract

With the development of deep learning, many solutions have achieved outstanding performance in solving facial recognition problems. Nevertheless, many challenges still stand, such as occluded face or illumination. This paper proposes a late fusion of many weighted weak classifiers to form a strong classifier for facial recognition. We train convolutional neural network models as weak classifiers on specific facial components. We build a strong classifier by lately fusing those weak classifiers with corresponding weights calculated locally or globally. A voting method is applied to determine the identity of the face. We experimented on five databases: ORL, CyberSoft, Georgia Tech, Essex Grimace and Essex Faces96. Performances of our method in those databases varied between 99% and 100%. Our proposed method can be used efficiently when a facial image only contains a few facial components. Also, our proposed global weights worked well on many facial databases.