{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003575086,0.0004787974,0.0003814686,0.000548006,0.0002038753,0.0004213807,0.0008074966,0.000512732,0.004568605],"category_scores_gemma":[0.0005986659,0.0001862407,0.0003635596,0.0002349845,0.0003089065,0.0007343139,0.0005090046,0.0002745811,0.001953452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002089363,"about_ca_system_score_gemma":0.000230858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001303899,"about_ca_topic_score_gemma":0.001382923,"domain_scores_codex":[0.9997795,0.00003581397,0.000007721081,0.00006898717,0.00008191491,0.00002607901],"domain_scores_gemma":[0.9998708,0.00003380704,0.00001148359,0.00003173044,0.00003617481,0.00001609341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003392702,0.00008215125,0.0005687969,0.0001166325,0.00004572907,0.0001570082,0.0001347769,0.003993983,0.3283626,0.002363415,0.00382355,0.6600121],"study_design_scores_gemma":[0.00009325314,0.0005573027,0.007491115,0.00004071408,0.0001698795,0.002909041,0.00016087,0.2983978,0.6585817,0.005109634,0.02633758,0.0001510891],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04653822,0.0003446264,0.9428594,0.0001142271,0.0001058396,0.0001112581,0.0002257707,0.005291249,0.004409404],"genre_scores_gemma":[0.3145272,0.000444679,0.6750293,0.0001512071,0.0001098357,0.000161263,0.0006381525,0.000369691,0.008568674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004568605,"threshold_uncertainty_score":0.01528352,"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."}}