{"id":"W2156342517","doi":"10.1109/ccece.2009.5090128","title":"Pipelined minutiae extraction from fingerprint images","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Minutiae; Fingerprint (computing); Computer science; Fingerprint recognition; Artificial intelligence; Pattern recognition (psychology); Extraction (chemistry); Feature extraction; Computer vision","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":[0.0002022775,0.0003407126,0.0003929804,0.0005102944,0.0001962079,0.0003853687,0.0006020218,0.0003676903,0.002495748],"category_scores_gemma":[0.000663889,0.0003200309,0.0003798344,0.0005381261,0.0001965496,0.0008065588,0.0003952776,0.0003887556,0.0006680309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002619939,"about_ca_system_score_gemma":0.000485559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009441327,"about_ca_topic_score_gemma":0.001450907,"domain_scores_codex":[0.9997559,0.00001627578,0.00001393394,0.0000542877,0.000125931,0.00003359363],"domain_scores_gemma":[0.9997955,0.00005332862,0.00003153511,0.00005705023,0.00005165672,0.00001093675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002911493,0.00004261734,0.0009177938,0.0002374627,0.00003431244,0.0001276129,0.00008194402,0.005019718,0.473475,0.002367971,0.001195192,0.5162092],"study_design_scores_gemma":[0.00006205905,0.0006750753,0.01059451,0.00005090089,0.00009599912,0.002006558,0.00005466954,0.2407611,0.7113872,0.004580233,0.02964696,0.00008468904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06636848,0.0009157617,0.9286461,0.00009276333,0.00007276538,0.00008459208,0.0001688998,0.002205733,0.001444842],"genre_scores_gemma":[0.3115774,0.0007910557,0.6820931,0.00006805647,0.00004279458,0.00007616455,0.0003808867,0.00008947236,0.004881116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002495748,"threshold_uncertainty_score":0.008349061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660523684209693,"score_gpt":0.2714412392367681,"score_spread":0.2548360023946711,"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."}}