Please use this identifier to cite or link to this item:
|Title:||Automatic interpretation and coding of face images using flexible models||Authors:||Taylor, Chris J.
Cootes, Timothy F.
|Keywords:||Face images||Issue Date:||1997||Publisher:||IEEE||Source:||Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 19 , no. 7 , 1997, pp. 743 - 756||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.||URI:||http://ktisis.cut.ac.cy/handle/10488/245||ISSN:||0162-8828||DOI:||10.1109/34.598231||Rights:||© Copyright 2009 IEEE – All Rights Reserved.|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Jul 15, 2017
WEB OF SCIENCETM
checked on Jul 10, 2017
Page view(s) 185
checked on Jul 26, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.