{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003452724,0.0003309122,0.0005460755,0.0006953411,0.0001842685,0.0005667512,0.000382542,0.0005072629,0.001049098],"category_scores_gemma":[0.0009546258,0.00022654,0.0004200922,0.0006275336,0.0002251367,0.00089221,0.000404721,0.0003793846,0.0008458928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001398127,"about_ca_system_score_gemma":0.0002602951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004476688,"about_ca_topic_score_gemma":0.0006831198,"domain_scores_codex":[0.9996933,0.0000501212,0.00001781547,0.00007676573,0.0001326145,0.00002943213],"domain_scores_gemma":[0.9994196,0.000173035,0.00008387263,0.0001248028,0.0001735069,0.00002523537],"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":[0.0007142496,0.0001798267,0.002706403,0.0001074113,0.00006461331,0.0001589849,0.00005576245,0.007519694,0.4321245,0.001181285,0.001289777,0.5538976],"study_design_scores_gemma":[0.00006082862,0.0005154622,0.01768523,0.0000252281,0.0001605979,0.001006446,0.00005674254,0.6472607,0.3290828,0.0009109592,0.003176352,0.00005871618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.176824,0.0007719232,0.8189004,0.0001636029,0.0001167345,0.00004191745,0.0001231197,0.001208217,0.001850093],"genre_scores_gemma":[0.6780674,0.000618032,0.3173908,0.00008871261,0.00009223622,0.00004623585,0.000355373,0.0001093114,0.003232021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001049098,"threshold_uncertainty_score":0.003509581,"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."}}