{"id":"W2026324054","doi":"10.1142/s0219467814500132","title":"Multibiometric System Using Level Set, Modified LBP and Random Forest","year":2014,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biometrics; Computer science; Local binary patterns; Artificial intelligence; Pattern recognition (psychology); Face (sociological concept); Iris recognition; Feature (linguistics); IRIS (biosensor); Set (abstract data type); Boundary (topology); Feature extraction; Random forest; Process (computing); Feature vector; Feature selection; Computer vision; Histogram; Image (mathematics); 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":[0.0009580181,0.00008105576,0.0001498492,0.001461667,0.00007755021,0.0003799576,0.0004440304,0.00004849903,7.224832e-7],"category_scores_gemma":[0.0002480569,0.00006737909,0.00006936325,0.000691311,0.00007176686,0.0004464511,0.0001088077,0.0001219049,7.791533e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002337577,"about_ca_system_score_gemma":0.00003004752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004929978,"about_ca_topic_score_gemma":0.00000593619,"domain_scores_codex":[0.9989182,0.00007368662,0.0003464177,0.0001355103,0.0004350524,0.00009108902],"domain_scores_gemma":[0.9986467,0.0001677132,0.0003050418,0.0001127638,0.0006722396,0.0000954921],"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":[0.0005133202,0.0006057068,0.05921838,0.0003611386,0.001321481,0.0003636304,0.003394019,0.000294099,0.02258727,0.6392623,0.002572841,0.2695058],"study_design_scores_gemma":[0.006612999,0.0001485083,0.09322198,0.000163948,0.00006016484,0.002212023,0.0001628561,0.8854356,0.001325404,0.005576523,0.00472956,0.0003503593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2361758,0.0003402972,0.7624679,0.000329327,0.0005620564,0.00003551856,0.000006658068,0.000008795962,0.00007366384],"genre_scores_gemma":[0.978177,0.0001546264,0.02138969,0.000131681,0.0001232568,3.235018e-7,0.000001536929,0.000003842012,0.00001806134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8851416,"threshold_uncertainty_score":0.366394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0491726033402247,"score_gpt":0.2956917217472233,"score_spread":0.2465191184069986,"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."}}