Analysis and synthesis of pose variations of human faces by a linear pcmap model and its application for pose–invariant face recognition system

A method of manifold representation for human faces with pose variations is proposed. Our model consists of mappings between 3D head angles and facial images sepa-rately represented in shape and texture, via sub-space mod-els spanned by principal components (PCs). Explicit map-pings to and from 3D head angles are used as processes of pose estimation and transformation, respectively. Gen-eralization capability to unknown head poses enables our model to continuously cover pose parameter space, pro-viding high approximation accuracy. The feasibility of this model is evaluated in a number of experiments. We also pro-pose a novel pose-invariant face recognition system using our model as the entry format for a gallery of known per-sons. Experimental results with 3D facial models recorded by a Cyberware scanner show that our model provides a superior recognition performance against pose variations, and that texture synthesis process is carried out correctly.