Automatic frontal face annotation and AAM building for arbitrary expressions from a single frontal image only

Statistically motivated approaches for the registration and tracking of non-rigid objects, such as the active appearance model (AAM), have become very popular. A major drawback of these approaches is that they require manual annotation of all training images which can be tedious and error prone. In this paper, a MPEG-4 based approach for the automatic annotation of frontal face images, having any arbitrary facial expression, from a single annotated frontal image is presented. This approach utilises the MPEG-4 based facial animation system to generate virtual images having different expressions and uses the existing AAM framework to automatically annotate unseen images. The approach demonstrates an excellent generalisability by automatically annotating face images from two different databases.