{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004075068,0.0001045054,0.0001613292,0.001000783,0.0004111388,0.0001580768,0.0003530483,0.0001227712,0.000002765265],"category_scores_gemma":[0.00009247316,0.00009037185,0.00004990141,0.003771926,0.00004359101,0.000281566,0.0001535906,0.0000856597,0.000006956718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005270105,"about_ca_system_score_gemma":0.00005149955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005340583,"about_ca_topic_score_gemma":0.00000143203,"domain_scores_codex":[0.9989442,0.00003777895,0.0002163916,0.0003341594,0.0002719518,0.0001955835],"domain_scores_gemma":[0.9992107,0.000156984,0.0000938466,0.0002742303,0.0001702103,0.00009407804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001191098,0.001128086,0.009313945,0.002140711,0.0002854563,0.0001258816,0.02088202,0.000122975,0.1304233,0.4138159,0.03021195,0.3914306],"study_design_scores_gemma":[0.003027302,0.0001620896,0.01271045,0.00008860729,0.00002407531,0.0003253031,0.0009959033,0.9449838,0.01906258,0.0002882941,0.01761591,0.0007156361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0885378,0.0004725295,0.9097533,0.0001379186,0.0004519854,0.0002630172,0.00001154053,0.0001532445,0.0002186346],"genre_scores_gemma":[0.8984808,0.00003058063,0.1008753,0.00009869553,0.00002264213,0.000004649768,0.000004795256,0.00000593583,0.0004766631],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9448609,"threshold_uncertainty_score":0.3685257,"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."}}