Fusion of lattice independent and linear features improving face identification

This paper proposes the fusion of lattice independent component analysis (LICA) features with linear features obtained from conventional methods. Specifically, we compute class conditional LICA, where separate endmembers are extracted from each class dataset. We find that this fusion approach improves systematically the recognition accuracy in face recognition applications. We report experimental results using seven state-of-the-art linear feature extraction algorithms on four public face databases using Extreme Learning Machines (ELMs) for the classification building algorithm.