{"id":"W2158635403","doi":"10.1109/itng.2008.254","title":"FES: A System for Combining Face, Ear and Signature Biometrics Using Rank Level Fusion","year":2008,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Linear discriminant analysis; Computer science; Principal component analysis; Pattern recognition (psychology); Rank (graph theory); Artificial intelligence; Signature (topology); Face (sociological concept); Identity (music); Identification (biology); Modalities; Logistic regression; Sensor fusion; Data mining; Machine learning; Speech recognition; Mathematics","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":[0.001257181,0.0005926523,0.0008719927,0.001297984,0.0003509123,0.0005883267,0.0009636726,0.0007841222,0.004028139],"category_scores_gemma":[0.001446255,0.0002072917,0.0003817434,0.0006208589,0.0003779107,0.001231225,0.000936083,0.0003663279,0.001748968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002941394,"about_ca_system_score_gemma":0.0003439655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001542884,"about_ca_topic_score_gemma":0.001835315,"domain_scores_codex":[0.9991566,0.0001551442,0.00006031027,0.0001569647,0.0003973522,0.00007370311],"domain_scores_gemma":[0.9994978,0.0001181877,0.00005995507,0.00008775479,0.0001992327,0.00003710969],"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":[0.0010701,0.0001754499,0.002827027,0.0001731877,0.0001483113,0.0001717336,0.0001347292,0.00867281,0.1946475,0.005077644,0.006986513,0.779915],"study_design_scores_gemma":[0.0001731302,0.001851048,0.01917488,0.0000744185,0.0003091302,0.002552968,0.0001637626,0.6030424,0.3223954,0.007954377,0.04195306,0.0003554889],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06414286,0.0005182856,0.9203029,0.0001760508,0.0001074528,0.0002601582,0.0003853778,0.01086887,0.003237954],"genre_scores_gemma":[0.4349364,0.0002871648,0.5550438,0.0001668386,0.0001178305,0.0001899614,0.0008288132,0.0001144923,0.008314776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004028139,"threshold_uncertainty_score":0.01347548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1146707648113765,"score_gpt":0.2842814238254874,"score_spread":0.1696106590141109,"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."}}