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Record W2164714910 · doi:10.1109/icip.2002.1037984

Color-based mouth shape tracking for synthesizing realistic facial expressions

2003· article· en· W2164714910 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

VenueProceedings - International Conference on Image Processing · 2003
Typearticle
Languageen
FieldComputer Science
TopicFace recognition and analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInitializationComputer scienceArtificial intelligenceComputer visionFace (sociological concept)Facial expressionCoding (social sciences)Pattern recognition (psychology)Process (computing)Mathematics

Abstract

fetched live from OpenAlex

Mouth shape analysis and synthesis play an important role for realistic facial expression generation. The conventional deformable template-based method requires a fairly accurate initial localization of the template because the energy minimization process only finds a local minimum. We present a new method for accurate mouth corner detection and mouth shape estimation using color information. The proposed method can deal with a variety of shapes for both open and closed positions of the mouth. Accurate mouth corner detection and mouth status determination (open and closed) increases the accuracy of template initialization. The extracted mouth shape parameters can be used for synthesizing realistic virtual facial expressions in model based coding. Experiments on video sequences demonstrate the advantage of the proposed algorithm.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.329
Teacher spread0.248 · 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