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Cited by 538

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Analysis and synthesis of facial image sequences using physical and anatomical models

Terzopoulos, D., Waters, K.·1993·IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

An approach to the analysis of dynamic facial images for the purposes of estimating and resynthesizing dynamic facial expressions is presented. The approach exploits a sophisticated generative model of the human face originally developed for realistic facial animation. The face model which may be simulated and rendered at interactive rates on a graphics workstation, incorporates a physics-based synthetic facial tissue and a set of anatomically motivated facial muscle actuators. The estimation of dynamical facial muscle contractions from video sequences of expressive human faces is considered. An estimation technique that uses deformable contour models (snakes) to track the nonrigid motions of facial features in video images is developed. The technique estimates muscle actuator controls with sufficient accuracy to permit the face model to resynthesize transient expressions.>

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Metadata

Authors
Terzopoulos, D., & Waters, K.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
1993-06
Volume
15
Issue
6
Pages
569-579
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2096076356
Cited by
538
References
26
FWCI
32.75
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Analysis and synthesis of facial image sequences using physical and anatomical models. https://app.cochat.ai/papers/10.1109/34.216726
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Terzopoulos, D., & Waters, K. (1993). Analysis and synthesis of facial image sequences using physical and anatomical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.216726

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

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.