Performance of active appearance model-based pose-robust face recognition

Abstract

In this study, the authors study the performance of a novel active appearance model (AAM)-based fully automatic system for pose robust face recognition that allows faster fitting to a no frontal view and generation of virtual views. The system follows a multiresolution scheme, where the first level is used to initialise a generic AAM, pose angle is automatically estimated using eigenvector analysis, and then a pose-dependent AAM model is selected. Next level refines AAM model fitting and registration. Finally, a virtual frontal view is created before face matching. Recognition results over CMU PIE database show similar values compared with the performance achieved with manually landmarked faces. Compared with a classical view-based approach, this multiresolution scheme performs similarly but is sensibly faster.