Automatic interpretation and coding of face images using flexiblemodels
Lanitis, A.
Taylor, C.J.
Cootes, T.F.
Dept. of Med. Biophys., Manchester Univ.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jul 1997
Volume: 19,
Issue: 7
On page(s): 743-756
ISSN: 0162-8828
References Cited: 39
CODEN: ITPIDJ
INSPEC Accession Number: 5661538
Digital Object Identifier: 10.1109/34.598231
Current Version Published: 2002-08-06
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
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