{"id":"W4211155612","doi":"10.1007/978-981-16-8129-5_111","title":"A Deep Learning Approach for Detecting Medical Face Mask on Human Faces in Response to Covid19","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Face (sociological concept); Computer science; Computer vision; Deep learning; Facial recognition system; Face detection; Image (mathematics); Face masks; Pattern recognition (psychology); Coronavirus disease 2019 (COVID-19); Medicine","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001004272,0.0004216542,0.0006016623,0.001477268,0.000138713,0.0000953534,0.0007787493,0.0004381072,0.00007957117],"category_scores_gemma":[0.003058592,0.0004393691,0.0002133428,0.0008081793,0.00001254775,0.00007199857,0.0002115377,0.002327351,0.000007603394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005618281,"about_ca_system_score_gemma":0.00008321311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001605436,"about_ca_topic_score_gemma":0.00003070611,"domain_scores_codex":[0.9972533,0.0001040158,0.000477069,0.0008515687,0.0006983656,0.0006156788],"domain_scores_gemma":[0.9974649,0.001913972,0.00009624763,0.0002942834,0.00003111167,0.0001994718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007066783,0.00002922147,0.000006153439,0.00004451292,0.00003227232,0.00005256477,0.0003902875,0.9031172,0.0008524874,0.002237606,0.00000230264,0.09316477],"study_design_scores_gemma":[0.0004485834,0.0003944884,0.00001125888,0.0001135683,0.00001226693,0.00001701518,0.000003224508,0.9921822,0.0004138027,0.0005280279,0.005337064,0.0005385062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006272915,0.00038216,0.9966832,0.0005023475,0.0000720244,0.0004374,0.000001822793,0.0002273743,0.001066387],"genre_scores_gemma":[0.9503145,0.00006704646,0.04647752,0.0009031246,0.0001842078,0.0003301238,0.00004331002,0.0001556514,0.001524484],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9502057,"threshold_uncertainty_score":0.9999743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551135039571623,"score_gpt":0.2498779596909579,"score_spread":0.2343666092952416,"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."}}