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Cited by 625

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Automatic interpretation and coding of face images using flexible models

Lanitis, A., Taylor, C.J., Cootes, T.F.·1997·IEEE Transactions on Pattern Analysis and Machine Intelligence

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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Metadata

Authors
Lanitis, A., Taylor, C. J., & Cootes, T. F.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
1997-07
Volume
19
Issue
7
Pages
743-756
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2129150631
Cited by
625
References
1
FWCI
28.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Automatic interpretation and coding of face images using flexible models. https://app.cochat.ai/papers/10.1109/34.598231
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Lanitis, A., Taylor, C. J., & Cootes, T. F. (1997). Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.598231

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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