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Record W2158535000 · doi:10.1109/imtc.2007.379320

3D Head Tracking and Facial Expression Recovery using an Anthropometric Muscle-based Active Appearance Model

2007· article· en· W2158535000 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueConference proceedings - IEEE Instrumentation/Measurement Technology Conference · 2007
Typearticle
Languageen
FieldComputer Science
TopicFace recognition and analysis
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaElse Kröner-Fresenius-Stiftung
KeywordsComputer visionArtificial intelligenceComputer scienceActive appearance modelFacial expressionPoseKalman filterFace (sociological concept)Extended Kalman filterFeature (linguistics)3d modelOrientation (vector space)Pattern recognition (psychology)MathematicsImage (mathematics)

Abstract

fetched live from OpenAlex

This paper describes a novel 3D model-based tracking algorithm allowing real-time recovery of 3D position, orientation and facial expressions of a moving head. The method uses a 3D anthropometric muscle-based active appearance model (3D AMB AAM), a feature-based matching algorithm, and an extended Kalman filter (EKF) pose and expression estimator. Our model is an extension of the classical 2D AAM, and uses a generic 3D wireframe model of the face, based on two sets of controls: the anatomically motivated muscle actuators to model facial expressions and statistically-based anthropometrical controls to model different facial types.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.114
GPT teacher head0.313
Teacher spread0.200 · 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