{"id":"W4293192750","doi":"10.1109/access.2022.3160828","title":"Masked Face Recognition From Synthesis to Reality","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Face recognition and analysis","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Ministry of Science and Technology, Taiwan; Ministry of Education, India; University of Calgary","keywords":"Softmax function; Computer science; Facial recognition system; Artificial intelligence; Face (sociological concept); Pattern recognition (psychology); Margin (machine learning); Benchmark (surveying); Embedding; Feature (linguistics); Computer vision; Deep learning; Speech recognition; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000583643,0.0007750553,0.000629894,0.0004524399,0.0001459593,0.00087595,0.0006670414,0.0006330223,0.004392772],"category_scores_gemma":[0.001979104,0.0002901299,0.0006839038,0.000219626,0.0003430762,0.0007298521,0.0009905649,0.0006802829,0.001813577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003596981,"about_ca_system_score_gemma":0.0004910818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002339038,"about_ca_topic_score_gemma":0.002078375,"domain_scores_codex":[0.9994311,0.00008255112,0.00002300895,0.0001483167,0.0002593355,0.00005567038],"domain_scores_gemma":[0.9997173,0.00007883037,0.00001741534,0.00009974904,0.00007211027,0.00001463973],"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":[0.0007065862,0.0001028539,0.0009034182,0.0004811496,0.0001376117,0.0004926974,0.0001687624,0.1166843,0.1940113,0.008378045,0.01176638,0.6661669],"study_design_scores_gemma":[0.00003327357,0.0004244885,0.002400592,0.00009022552,0.00005812013,0.001030767,0.00009337545,0.8891072,0.07981418,0.009199384,0.01769488,0.00005351199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08853469,0.002908485,0.8909911,0.000438378,0.0005436964,0.0001681639,0.001180641,0.004901056,0.01033374],"genre_scores_gemma":[0.5285352,0.002231767,0.4540367,0.000516126,0.000220048,0.0001772336,0.003685492,0.0004205401,0.01017694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004392772,"threshold_uncertainty_score":0.01469529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0833087369586954,"score_gpt":0.3128499044629806,"score_spread":0.2295411675042852,"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."}}