Synthesized virtual view-based eigenspace for face recognition

This paper presents a new face recognition method using virtual view-based eigenspace. This method provides a possible way to recognize human face of different views even when samples of a view are not available. To achieve this, we have developed a virtual human face generation technique that synthesizes human face of arbitrary views. By using a frontal and profile images of a specific subject, a deformation technique allows automatic alignment of features in the 3-D generic graphic face model with the features of the pre-provided images of the specific subject. The deformation result is a 3-D face model of the specific human face. It reflects accurately the correspondence geometric features and texture features of the specific subject. In the recognition step, we use an extended nearest-neighbor rule based on an Euclidean distance measure as the recognition classifier. This work shows the feasibility of applying 3-D modeling techniques onto face recognition problems.