{"id":"W4220867191","doi":"10.18280/mmep.090135","title":"Machine Learning for Masked Face Recognition in COVID-19 Pandemic Situation","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Face recognition and analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Forehead; Facial recognition system; Computer science; Support vector machine; Three-dimensional face recognition; Pattern recognition (psychology); Classifier (UML); Face (sociological concept); Coronavirus disease 2019 (COVID-19); Computer vision; Face detection; Random forest; Speech recognition; Infectious disease (medical specialty); Medicine; Disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006732794,0.0004442217,0.0005425587,0.0005380564,0.0003204302,0.0003662299,0.0005002233,0.0005764142,0.001210348],"category_scores_gemma":[0.001647568,0.0001304995,0.00051347,0.0002949585,0.0001806617,0.0005482485,0.0003452807,0.0005529478,0.0005942253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003020442,"about_ca_system_score_gemma":0.0004008751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004655128,"about_ca_topic_score_gemma":0.003522324,"domain_scores_codex":[0.9995643,0.00008972519,0.00002724418,0.0001131307,0.0001224153,0.00008312077],"domain_scores_gemma":[0.9996347,0.0001534207,0.00003407283,0.00004499497,0.0001157475,0.00001698692],"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":[0.000687735,0.0004081731,0.01326595,0.000143833,0.0001095295,0.0004815738,0.0002104512,0.108203,0.03680135,0.001794245,0.008620316,0.8292739],"study_design_scores_gemma":[0.000006439515,0.00009538317,0.005140732,0.0000100254,0.0000146293,0.0001697315,0.00007030297,0.9851436,0.007137251,0.001397007,0.0008029334,0.0000119834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4996808,0.001940217,0.4902246,0.0009388607,0.0003161361,0.000123744,0.0005502659,0.001871385,0.004354003],"genre_scores_gemma":[0.9277601,0.0003816671,0.06843799,0.000133755,0.00006344185,0.00004942601,0.0006913841,0.00002775503,0.002454455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004655128,"threshold_uncertainty_score":0.009256065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06723095325287544,"score_gpt":0.2532495401442533,"score_spread":0.1860185868913778,"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."}}