{"id":"W2118271890","doi":"10.1109/fuzzy.2010.5584450","title":"Enhanced weakly trained frontal face detector for surveillance purposes","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Detector; Artificial intelligence; Histogram; Computer science; Face (sociological concept); Face detection; False positive rate; Pattern recognition (psychology); Computer vision; Histogram of oriented gradients; Haar-like features; Object-class detection; Facial recognition system; Image (mathematics); Telecommunications","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":[0.0001359337,0.0001065202,0.0001196377,0.00003945538,0.0001019201,0.0001044624,0.0004286687,0.00007501649,0.0001398705],"category_scores_gemma":[0.00008399385,0.0000851669,0.00006937486,0.0000926987,0.00002262022,0.0003433558,0.00005906085,0.00009594364,0.0001176058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000639031,"about_ca_system_score_gemma":0.0000321454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001718443,"about_ca_topic_score_gemma":0.0003072411,"domain_scores_codex":[0.9991843,0.00001659658,0.0001377446,0.0003019019,0.0001269208,0.0002325535],"domain_scores_gemma":[0.9993855,0.0001245528,0.00004586541,0.0002865076,0.0000748219,0.00008270318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001482137,0.00002862887,0.00002835234,0.000006540489,0.00000474551,4.887898e-7,0.0001643016,0.000002702225,0.9068231,0.001187741,0.002778891,0.08895968],"study_design_scores_gemma":[0.0006759218,0.0001196625,0.002138344,0.000007601615,0.000001461786,0.000004729339,0.00006064908,0.01629263,0.964795,0.001510546,0.01413285,0.0002605863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2842661,0.000009768712,0.7115942,0.0005769462,0.0007471191,0.0002541101,0.00001041731,0.0002190632,0.002322327],"genre_scores_gemma":[0.9231703,0.000002811985,0.07475368,0.0002469999,0.00008225083,0.00007572715,0.000006675345,0.000006904906,0.001654655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6389042,"threshold_uncertainty_score":0.3473005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014952682784928,"score_gpt":0.2385279268955054,"score_spread":0.2283784000676561,"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."}}