{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005646946,0.0004072651,0.0004931375,0.0008122671,0.0002959326,0.0005701315,0.001166462,0.0008249945,0.003474168],"category_scores_gemma":[0.001186763,0.0003165178,0.0002523902,0.0003958383,0.0002619534,0.0007550486,0.0005624252,0.0005974051,0.002195792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002925263,"about_ca_system_score_gemma":0.0004888922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002133814,"about_ca_topic_score_gemma":0.002626188,"domain_scores_codex":[0.9994857,0.00003987708,0.00001669953,0.0001774406,0.0002353719,0.00004489431],"domain_scores_gemma":[0.9994172,0.0001444535,0.00007808939,0.0001076435,0.0002024573,0.00005032703],"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":[0.0002921741,0.0001779348,0.00353024,0.0001586571,0.00003220209,0.0001215555,0.00009151756,0.003414001,0.3521139,0.001804263,0.005773454,0.6324902],"study_design_scores_gemma":[0.0001478904,0.001079221,0.04413465,0.00007564972,0.0001131294,0.002899161,0.0001114102,0.2861442,0.5881963,0.004751285,0.0721703,0.0001769637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03058898,0.0006803528,0.960294,0.0001347807,0.000143855,0.0001630383,0.0004920752,0.003355598,0.004147336],"genre_scores_gemma":[0.1858607,0.0008000845,0.8002201,0.0002326244,0.0001303762,0.0004004104,0.00120612,0.0002151354,0.01093446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003474168,"threshold_uncertainty_score":0.01162225,"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."}}