Abstract—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.

Detection of facial features is difficult because face images are highly variable. Sources of variability include individual appearance, 3D pose, facial expression and lighting. We describe a system for locating facial features, which uses flexible models of appearance as a means of providingprior knowledge about the expected appearance of j'katures and the spatial relationships between them. A global search procedure based on Genetic Algorithms is used to fit the model to new face images, so that the exact position of a face and facial features can be recovered.

Point Distribution Models (PDMs) are statistical models which represent objects whose shape can vary. A useful feature of PDMs is their ability to capture the shape of variable objects within a training set with a small number of shape parameters. This compact and accurate parametrization can be used for the design of efficient classification systems. In this paper we describe a classification system which uses shape parameters. We have tested the system on classifying hand outlines, face outlines and hand gestures; experimental results are presented.