General Pose Face Recognition Using Frontal Face Model

We present a face recognition system able to identify people from a single non-frontal image in an arbitrary pose. The key component of the system is a novel pose correction technique based on Active Appearance Models (AAMs), which is used to remap probe images into a frontal pose similar to that of gallery images. The method generalises previous pose correction algorithms based on AAMs to multiple axis head rotations. We show that such model can be combined with image warping techniques to increase the textural content of the images synthesised. We also show that bilateral symmetry of faces can be exploited to improve recognition. Experiments on a database of 570 non-frontal test images, which includes 148 different identities, show that the method produces a significant increase in the success rate (up to 77.4%) compared to conventional recognition techniques which do not consider pose correction.