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Record W1842855933 · doi:10.1109/icpr.1998.711104

Facial muscle parameter decision from 2D frontal image

2002· article· en· W1842855933 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFace recognition and analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceArtificial intelligenceComputer visionFace (sociological concept)Facial musclesArtificial neural networkImage (mathematics)Pattern recognition (psychology)Facial expressionAnatomyBiology

Abstract

fetched live from OpenAlex

Muscle based face image synthesis is one of the most realistic approaches to realizing life-like agents in a computer. A facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue elements by contraction of each muscle strength, so the combination of each muscle parameter decides a specific facial expression. Each muscle parameter is decided based on a trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific face image. We propose a strategy of automatic estimation of facial muscle parameters from 2D marker movements using a neural network. We can also carry out 3D motion estimation from 2D point or flow information in a captured image under restriction of a physics based face model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.232
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations13
Published2002
Admission routes1
Has abstractyes

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