A Hierarchical Face Identification System Based on Facial Components

It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific face. This paper addresses a novel decision strategy that efficiently uses both holistic and facial component (left eye, right eye, nose and mouth) feature analysis to recognize faces. The proposed algorithm uses the whole face features in the first step of recognition task. If the decision machine fails to assign a class (with high confidence) then the individual facial components are processed and the resulting information are combined with those obtained from the whole face to assign the output. Simulation studies justify the superior performance of the proposed method as compared to that of Eigenface method. Experimental results also show that the proposed system is robust against small errors in facial component extractor.