{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009614982,0.00004698067,0.00005050701,0.00004419364,0.00009086677,0.0001514366,0.0002106138,0.0000342337,0.00000132206],"category_scores_gemma":[0.00002282342,0.00003914289,0.00002718528,0.00009030631,0.000002951188,0.001709826,0.00002732541,0.00003171169,0.0000538193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001743706,"about_ca_system_score_gemma":0.00001311541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007215957,"about_ca_topic_score_gemma":2.295809e-7,"domain_scores_codex":[0.9995498,0.000008899644,0.0001161528,0.00008316764,0.000126238,0.0001157236],"domain_scores_gemma":[0.9996976,0.00002358975,0.00003716999,0.0001271533,0.00007633643,0.00003816302],"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":[0.00003700406,0.00005676685,0.0002466516,0.0001422391,0.000007172755,0.000001614564,0.00511509,0.0001984092,0.005356048,0.1025654,0.01094566,0.8753279],"study_design_scores_gemma":[0.001978129,0.0005080183,0.00560265,0.0004247998,0.00001010901,0.00002521256,0.00222871,0.4819941,0.4369994,0.01723372,0.05229253,0.0007027062],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03392876,0.000009739991,0.9533058,0.0005571408,0.0007384807,0.0003106999,0.000003300859,0.0003952254,0.0107509],"genre_scores_gemma":[0.9826969,0.000001543852,0.01710386,0.00006821995,0.00007118803,0.0000190591,0.00000225652,6.450258e-7,0.00003636298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9487681,"threshold_uncertainty_score":0.1596201,"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."}}