{"id":"W1958778584","doi":"10.1109/icip.1999.821717","title":"Detection and tracking of faces and facial features","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Army Research Laboratory","keywords":"Artificial intelligence; Computer science; Computer vision; Face detection; Feature (linguistics); Tracking (education); Facial expression; Facial motion capture; Face hallucination; Face (sociological concept); Detector; Feature extraction; Pattern recognition (psychology); Set (abstract data type); Facial recognition system; Tracking system; Kalman filter","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":[0.00005216431,0.00002977419,0.00003931029,0.00002870471,0.00004203416,0.00003749736,0.00002838918,0.00002434249,0.000007658736],"category_scores_gemma":[0.00001689674,0.00002284894,0.000006506017,0.00004455694,0.00001404107,0.0002070804,0.00001450615,0.00002516901,7.552107e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000110395,"about_ca_system_score_gemma":0.000002858847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001595274,"about_ca_topic_score_gemma":0.00002141119,"domain_scores_codex":[0.9997606,0.0000159932,0.00004393023,0.00008692047,0.0000477097,0.00004482801],"domain_scores_gemma":[0.9998922,0.00001721322,0.00001704079,0.00004024497,0.00001502564,0.00001832117],"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":[0.000004141496,0.00001543815,0.001530244,0.00001847994,0.000004677114,8.214082e-7,0.0008919874,0.00000473147,0.2170021,0.004058049,0.0001460569,0.7763233],"study_design_scores_gemma":[0.000236625,0.00006159862,0.02253077,0.00001512816,0.000002451307,0.00002194171,0.0002202132,0.0009762027,0.969552,0.004325518,0.001974405,0.00008315914],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9120635,0.0001683361,0.08411742,0.00006428968,0.00007592079,0.00004069647,3.235784e-7,0.00002817287,0.003441276],"genre_scores_gemma":[0.9948685,0.00003028635,0.004966937,0.00003636544,0.000003560002,9.880475e-7,6.847465e-8,8.130509e-7,0.00009253573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7762401,"threshold_uncertainty_score":0.09317529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129972684065183,"score_gpt":0.2298329814738825,"score_spread":0.2168357130673642,"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."}}