查看文章

This research presents a study of the geometry of the face manifold as a person changes their horizontal pose from one profile to another. Although, a lot of research has gone into aspects of determining an ideal pose for pose invariant face recognition, less has been done to present the manifold of the faces presented by these pose variations. The novelty of our approach lies in the presentation of a finely sampled profile-to-profile dataset that is analyzed using Locally Linear Embedding (LLE) to estimate the curvature of these manifolds. Our results indicate that the profile-to-profile manifold is less curved, hence more linear, in the region around the frontal view than for any other region of the manifold, i.e. pose.