An image face modeling framework is proposed that aims to enhance the face modeling capability of the well known Active Appearance Model (AAM). AAM has been used successfully in person-specific related applications but it poses significant limitations when employed in generic face modeling. Thus this work is focused on the development of new face models which are generic in nature and which accurately fit unseen image faces, both in terms of shape and texture. For this purpose, images are decomposed into face related components which are subsequently clustered on the basis of shape similarities. Experimental results show that models generated through this novel framework can be significantly more effective than conventional AAM, in terms of both shape and texture.
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