{"id":"W2111250069","doi":"10.1109/isspit.2006.270906","title":"Video Tracking Of 2D Face Motion During Speech","year":2006,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Face (sociological concept); Tracking (education); Frame (networking); Digital video; Motion (physics); Sequence (biology); Block-matching algorithm; Facial motion capture; Video tracking; Speech recognition; Facial recognition system; Face detection; Pattern recognition (psychology); Video processing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002256755,0.0002735816,0.0002977761,0.0006026206,0.000166662,0.0003882387,0.0004413861,0.0005114802,0.001464436],"category_scores_gemma":[0.00100355,0.0001875583,0.0001410885,0.0003489605,0.0001201925,0.0003134855,0.0002722737,0.0002253143,0.0005563908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002005881,"about_ca_system_score_gemma":0.0001765388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583516,"about_ca_topic_score_gemma":0.002195669,"domain_scores_codex":[0.999809,0.00002815296,0.000007122502,0.00006512,0.00007110032,0.00001942074],"domain_scores_gemma":[0.9997041,0.0001089988,0.00003216896,0.00003880602,0.00009375669,0.00002213071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004049033,0.00008044187,0.003119069,0.0001486975,0.00003157208,0.0002046041,0.0002116037,0.004448898,0.7550563,0.0004055306,0.00113337,0.234755],"study_design_scores_gemma":[0.00009519082,0.0007284135,0.05942063,0.00005116564,0.00007807969,0.002236584,0.0001397929,0.2201727,0.7073429,0.000642478,0.009003256,0.00008875468],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4308609,0.0009183391,0.5603279,0.000102824,0.0001331146,0.0002220122,0.001166509,0.001944732,0.004323705],"genre_scores_gemma":[0.69568,0.0005355756,0.2991314,0.0000863618,0.00006362401,0.0001819159,0.0009598015,0.0001291689,0.003232099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001583516,"threshold_uncertainty_score":0.004898965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109762927479509,"score_gpt":0.2266138734045881,"score_spread":0.2156375806566372,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}