{"id":"W2058138187","doi":"10.1109/conielecomp.2012.6189911","title":"Evaluation of machine learning techniques for face detection and recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial intelligence; Facial recognition system; Preprocessor; Face (sociological concept); Three-dimensional face recognition; Face detection; Biometrics; Object-class detection; Computer vision; Field (mathematics); Pattern recognition (psychology); Identification (biology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001337765,0.00004475281,0.00005269799,0.00006806922,0.00005954768,0.00001736274,0.00004247513,0.00004087239,0.00001758027],"category_scores_gemma":[0.0001365334,0.0000383461,0.00001823697,0.0000753139,0.000007944444,0.000568476,0.00002701839,0.00003914228,0.000004757182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001417384,"about_ca_system_score_gemma":0.000007525734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002086078,"about_ca_topic_score_gemma":0.0000057889,"domain_scores_codex":[0.9994503,0.00008636727,0.00009801402,0.00009427255,0.0001843195,0.00008668374],"domain_scores_gemma":[0.999576,0.00005276725,0.00006334294,0.0000616731,0.0002187428,0.00002746972],"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":[0.000003714656,0.00001736773,0.000104005,0.000009320343,0.000003122494,3.754414e-9,0.0001982842,0.000002860092,0.07300434,0.000041657,0.00001653796,0.9265988],"study_design_scores_gemma":[0.000210538,0.00009686124,0.0005051165,0.00002033016,0.00002204565,0.000003367028,0.00006483725,0.1678519,0.8268555,0.003720337,0.0005816665,0.00006744608],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1421857,0.0001453397,0.8560758,0.00005407395,0.00007564649,0.0002850947,0.000001366838,0.0001016122,0.001075404],"genre_scores_gemma":[0.9488796,0.00002499278,0.05092715,0.00002423897,0.00002569502,0.00006986135,0.000006665124,0.000002974593,0.00003881629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9265314,"threshold_uncertainty_score":0.1563708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06189716171133764,"score_gpt":0.309734164209718,"score_spread":0.2478370024983804,"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."}}