{"id":"W2125403238","doi":"10.1109/cibim.2011.5949217","title":"Using fuzzy adaptive fusion in face detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Artificial intelligence; Computer science; Face (sociological concept); Face detection; Computer vision; Object-class detection; Facial recognition system; Detector; Pattern recognition (psychology); Fuzzy logic; Set (abstract data type); Fuzzy set; Process (computing); Image (mathematics)","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":[0.0011873,0.0003592161,0.0005551364,0.0007434092,0.0003995657,0.0005288888,0.0006902872,0.0006653779,0.0005955154],"category_scores_gemma":[0.001609917,0.0002565978,0.0005409335,0.0005639892,0.000463987,0.00076199,0.0006446074,0.0005215043,0.000215582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004624129,"about_ca_system_score_gemma":0.0002792759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002070547,"about_ca_topic_score_gemma":0.001755506,"domain_scores_codex":[0.9993991,0.0001047278,0.00003083599,0.0001351596,0.0002783024,0.00005193221],"domain_scores_gemma":[0.9996004,0.0001550411,0.00003850287,0.00003790524,0.0001518776,0.00001630524],"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":[0.0004648155,0.0001845459,0.002236884,0.0001583209,0.0001461642,0.0002371807,0.0002752358,0.2003225,0.1225006,0.009463947,0.001012762,0.6629971],"study_design_scores_gemma":[0.00001302716,0.0001572582,0.001303789,0.00001466797,0.00003858911,0.0001293422,0.00002617557,0.9711264,0.02226692,0.003665023,0.001226279,0.00003253113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05491821,0.0004246689,0.9422256,0.00006887042,0.00005754229,0.00004030922,0.00001308824,0.0002578931,0.001993743],"genre_scores_gemma":[0.7746689,0.0002742861,0.2235588,0.00006131197,0.00004597243,0.00004930423,0.00003207529,0.00001735943,0.001291953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002070547,"threshold_uncertainty_score":0.006279111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1022298588966273,"score_gpt":0.2638070988943621,"score_spread":0.1615772399977348,"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."}}