{"id":"W2166857861","doi":"10.1109/cib.2009.4925689","title":"A facial presence monitoring system for information security","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; Defence Research and Development Canada","funders":"Defence Research and Development Canada","keywords":"Computer science; Eigenface; Biometrics; Facial recognition system; Session (web analytics); Human–computer interaction; Graphical user interface; Face detection; User interface; Identity (music); Face (sociological concept); Information security; Computer security; Feature extraction; Artificial intelligence; Operating system; World Wide Web","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":[0.0005793339,0.000420949,0.0005036194,0.0004657359,0.0004566653,0.0004807354,0.0008005641,0.0006778451,0.01378012],"category_scores_gemma":[0.001315762,0.0002021142,0.0002336501,0.000253807,0.0001903841,0.0007505993,0.0005382031,0.0005225734,0.003212987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004522473,"about_ca_system_score_gemma":0.0004974116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001013397,"about_ca_topic_score_gemma":0.001307128,"domain_scores_codex":[0.9995396,0.00007235914,0.00001840144,0.0001119717,0.0002288012,0.00002894519],"domain_scores_gemma":[0.9995726,0.00008009913,0.00003690371,0.00007612806,0.0001897638,0.00004449506],"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":[0.001173829,0.0003731534,0.004123105,0.0002887545,0.00004826461,0.0003797518,0.0002194687,0.002089213,0.4803475,0.003470934,0.01834749,0.4891386],"study_design_scores_gemma":[0.0003156185,0.002639833,0.03697223,0.0001750586,0.000310827,0.004739471,0.0001041574,0.3664575,0.4789959,0.002653945,0.1063876,0.0002479585],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1083762,0.0007028065,0.8412982,0.0006783243,0.0004336341,0.001018043,0.0008493921,0.02374214,0.02290132],"genre_scores_gemma":[0.553708,0.0004522645,0.4193952,0.000541639,0.0001313073,0.0007675924,0.0008627529,0.0003212306,0.02382001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01378012,"threshold_uncertainty_score":0.04609907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452683270307475,"score_gpt":0.2516624470491514,"score_spread":0.2371356143460766,"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."}}