Automatic interpretation and coding of face images using flexiblemodels
Lanitis, A.; Taylor, C.J.; Cootes, T.F.
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume 19, Issue 7, Jul 1997 Page(s):743 - 756
Digital Object Identifier 10.1109/34.598231
Summary: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
View citation and abstract |
Cart
