Abstract

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appear# ance, 3D pose, facial expression and lighting. We describe a compact parametrised model of facial appearance which takes into account all these sources of variability. The model represents both shape and grey-level appearance and is created by performing a statistical analysis over a training set of face images. A robust multi-resolution search algo# rithm is used to fit the model to faces in new images. This allows the main facial features to be located and a set of shape and grey-level appearance parameters to be recov# ered. A good approximation to a given face can be recon# structed using less than 100 of these parameters. This repre# sentation can be used for tasks such as image coding, person identification, pose recovery, gender recognition and ex# pression recognition. The system performs well on all the tasks listed above. 1: Introduction Á ÂÄÀÅÃÇÂÉÀÊÅËÂÈ...

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