{"id":"W2126375223","doi":"10.1142/s0218001410008421","title":"IMPROVEMENT OF IRIS RECOGNITION PERFORMANCE USING REGION-BASED ACTIVE CONTOURS, GENETIC ALGORITHMS AND SVMs","year":2010,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Iris recognition; Computer science; Artificial intelligence; IRIS (biosensor); Computer vision; Support vector machine; Pattern recognition (psychology); Matching (statistics); Motion blur; Process (computing); Genetic algorithm; Algorithm; Biometrics; Image (mathematics); Mathematics; Machine learning","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":[0.00190271,0.0006839419,0.0009058175,0.000876661,0.0002177266,0.0007825888,0.0008301254,0.000767796,0.0006267949],"category_scores_gemma":[0.00477675,0.0002350735,0.0004808994,0.000734069,0.0003745286,0.001178926,0.0004375264,0.0007815325,0.0003935702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003832521,"about_ca_system_score_gemma":0.0004003723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002107563,"about_ca_topic_score_gemma":0.001498205,"domain_scores_codex":[0.9993054,0.0001759744,0.00005516656,0.0001483714,0.0002663757,0.00004878052],"domain_scores_gemma":[0.9977618,0.001101554,0.0002588708,0.000243294,0.0005908101,0.00004371303],"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":[0.0004445361,0.0001903581,0.003701246,0.0001150396,0.00009590929,0.00008949658,0.0001155624,0.1919076,0.03769463,0.002231829,0.001300298,0.7621135],"study_design_scores_gemma":[0.000008832846,0.00005024961,0.0007606377,0.000003935448,0.00001110898,0.00004012921,0.00001000381,0.987909,0.01043949,0.0004033373,0.0003553078,0.000007922411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09460129,0.0006002258,0.9012563,0.0001973927,0.00006264898,0.00005261153,0.00004398299,0.00185543,0.001330255],"genre_scores_gemma":[0.5667412,0.0002997594,0.4310105,0.00007783072,0.00003623519,0.00004922129,0.0001249004,0.00009919258,0.001561289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002107563,"threshold_uncertainty_score":0.01006263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09164895539831534,"score_gpt":0.3088363613109581,"score_spread":0.2171874059126427,"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."}}