Automatic interpretation and coding of face images using flexible models

Automatic interpretation and coding of face images using flexible models (1997)

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by Andreas Lanitis , Chris J. Taylor , Timothy F. Cootes

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	150 - 9 self

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Datum	Value	Source
TITLE	Automatic interpretation and coding of face images using flexible models	INFERENCE
AUTHOR NAME	Andreas Lanitis	SVM HeaderParse 0.2
AUTHOR NAME	Chris J. Taylor	SVM HeaderParse 0.2
AUTHOR NAME	Timothy F. Cootes	SVM HeaderParse 0.2
ABSTRACT	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.	SVM HeaderParse 0.2
YEAR	1997	INFERENCE
VENUE	IEEE Transactions on Pattern Analysis and Machine Intelligence	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	743--756	INFERENCE
VOLUME	19	INFERENCE
CITATIONS	39 found	ParsCit 1.0