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<p><b>Abstract</b>—Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression , and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance , and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm 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 gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition , and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting , and facial expression. The system performs well on all the tasks listed above.</p>
Face recognition, expression recognition, pose recovery, coding-reconstruction, facial feature location, deformable templates.
Chris J. Taylor, Timothy F. Cootes, Andreas Lanitis, "Automatic Interpretation and Coding of Face Images Using Flexible Models", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 743-756, July 1997, doi:10.1109/34.598231
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