{"id":"W2094505522","doi":"10.1007/s11265-014-0911-2","title":"Iris Recognition using Robust Localization and Nonsubsampled Contourlet Based Features","year":2014,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Contourlet; Artificial intelligence; Pattern recognition (psychology); Iris recognition; Computer science; Computer vision; Support vector machine; Feature extraction; Feature selection; Feature vector; Biometrics; Wavelet transform","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":[0.001466125,0.00009963215,0.0002201733,0.000453425,0.0002048508,0.0007468893,0.0002392012,0.00008669162,0.000004883112],"category_scores_gemma":[0.0001308459,0.00008288254,0.00004357588,0.0006845344,0.00004162709,0.0006963724,0.000019443,0.0001442882,0.000002512247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005723619,"about_ca_system_score_gemma":0.0001271059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004092379,"about_ca_topic_score_gemma":0.000001872792,"domain_scores_codex":[0.9985986,0.0002137205,0.0004783491,0.0001597619,0.0004194177,0.0001301756],"domain_scores_gemma":[0.9981408,0.0001120996,0.0007694144,0.00009770958,0.0007767065,0.0001032909],"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":[0.000232534,0.0006865758,0.006643233,0.003020387,0.0001672267,0.00005780272,0.003809459,0.05775684,0.03686164,0.001916282,0.008456403,0.8803916],"study_design_scores_gemma":[0.0005730176,0.00007329556,0.0004831546,0.0003935718,0.0000240109,0.0002238652,0.00008456285,0.994846,0.0005359442,0.0002394551,0.002395689,0.0001273867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009515581,0.001163872,0.9886997,0.0001646873,0.0003016146,0.00007699701,0.00000167208,0.00002538671,0.00005049109],"genre_scores_gemma":[0.9704611,0.000006488482,0.02914935,0.0001358965,0.0002214082,9.752033e-7,0.000003508207,0.000007129134,0.00001419647],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9609455,"threshold_uncertainty_score":0.7202271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843114347158269,"score_gpt":0.2589170760828741,"score_spread":0.2104859326112914,"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."}}