Orthogonal discriminant vector for face recognition across pose
Pattern Recognition
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In this paper we present a fully automatic system for face recognition across pose where no frontal view is needed in enrollment or test. The system uses three Active Appearance Models(AAMs): the first one is a generic multi resolution AAM, while the remaining ones are trained to cope with left/right variations (i.e. pose-dependent AAMs). During the fitting stage, pose is automatically estimated using eigenvector analysis, and a synthetic face is generated through texture warping. Results over CMU PIE Database show promising results compared to the performance achieved with manually land marked faces.