{"id":"W1849347704","doi":"10.1109/icip.2001.958280","title":"Talking face: using facial feature detection and image transformations for visual speech","year":2002,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Morphing; Artificial intelligence; Computer vision; Feature (linguistics); Face (sociological concept); Set (abstract data type); Image (mathematics); Frame (networking); Transformation (genetics); Feature detection (computer vision); Speech recognition; Image 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006180967,0.00006658264,0.00007412257,0.0001067848,0.0002342307,0.0002050742,0.00006924509,0.0000440243,0.00005065007],"category_scores_gemma":[0.000009883276,0.00006001968,0.0000603962,0.0002405357,0.00001454863,0.0006660896,0.00001423765,0.00005440024,0.0000130506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001574627,"about_ca_system_score_gemma":0.000003723136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007581024,"about_ca_topic_score_gemma":0.00003107797,"domain_scores_codex":[0.9995489,0.00001229739,0.00008690621,0.0001393629,0.00009015519,0.0001224541],"domain_scores_gemma":[0.9998124,0.00001782205,0.00002415391,0.00005618613,0.00004603775,0.00004343628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001719343,0.00003503101,0.0000122766,0.00002017611,0.00002167034,0.000001237423,0.000760562,0.00004547787,0.06715423,0.0002037423,0.0001762787,0.9315676],"study_design_scores_gemma":[0.0002345718,0.00002258249,0.00004284044,0.000004691779,0.00001369,0.00002180034,0.0001287908,0.9693533,0.02826973,0.000153616,0.001656839,0.00009759868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0491185,0.00002734552,0.9491538,0.0005944871,0.00005715669,0.0001125334,0.000004157217,0.00008634505,0.0008456677],"genre_scores_gemma":[0.8890381,0.00002513976,0.1102305,0.0001538053,0.00004070807,0.000006158326,0.00000245306,0.000004367436,0.0004986922],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9693078,"threshold_uncertainty_score":0.2447531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02079112845352775,"score_gpt":0.2698452080618286,"score_spread":0.2490540796083008,"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."}}