{"id":"W4395660503","doi":"10.46254/ba06.20230105","title":"Computer Vision Based Automated Attendance System Using Face Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Facial recognition system; Computer science; Computer vision; Artificial intelligence; Face (sociological concept); Face detection; Attendance; Pattern recognition (psychology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003451538,0.0003933364,0.0007133622,0.001029577,0.0003684061,0.0005730999,0.00083021,0.000702869,0.004998247],"category_scores_gemma":[0.0005835943,0.0001654624,0.0005459535,0.0004807915,0.0001545062,0.0005154111,0.0004580432,0.0005086901,0.002646916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003510979,"about_ca_system_score_gemma":0.0004104534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002354574,"about_ca_topic_score_gemma":0.001936432,"domain_scores_codex":[0.9995603,0.00004668707,0.00001629638,0.0001086557,0.0002120277,0.00005606126],"domain_scores_gemma":[0.9997579,0.00003189815,0.00003193876,0.00002764425,0.0001342881,0.00001627881],"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":[0.0003169624,0.0002798303,0.00361897,0.0002604093,0.00007356041,0.0002758914,0.0001121519,0.01134111,0.1979554,0.001716055,0.01045266,0.773597],"study_design_scores_gemma":[0.00008239509,0.001215258,0.03198548,0.0001069447,0.0002059421,0.001947265,0.0002199442,0.6843575,0.2484698,0.002598085,0.02860852,0.0002029407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1029307,0.0008107045,0.8643731,0.0003156227,0.0003368857,0.0003224424,0.0006910883,0.01484285,0.01537648],"genre_scores_gemma":[0.705162,0.0008179651,0.2695457,0.000293037,0.0001689432,0.0002972798,0.0009743915,0.000163311,0.02257728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004998247,"threshold_uncertainty_score":0.01672077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03907385321158705,"score_gpt":0.2860088335270875,"score_spread":0.2469349803155005,"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."}}