A Robust Multimodal Face Recognition Algorithm

It is always a difficult problem in face recognition on how to process the multimodal information (e.g. variation in lighting or orientation). Traditional graph embedding algorithms neglect congener correlation between different multimodal clusters of the same class (i.e. subject) and do not properly incorporate discriminative information between classes. In this paper, a robust graph embedding face recognition algorithm is proposed. It properly captures multimodal structure within one class and also realizes a balance between local manifold structures and the global discriminative information. Experiments in several public databases demonstrate that the proposed algorithm can achieve better performance than the state-of-arts reported in recent literatures.