{"id":"W2064553809","doi":"10.1007/s11265-011-0630-x","title":"A Filter Bank Based Approach for Rotation Invariant Fingerprint Recognition","year":2011,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Principal component analysis; Thresholding; Gabor filter; Computer science; Fingerprint (computing); Linear discriminant analysis; Dimensionality reduction; Filter bank; Filter (signal processing); Computer vision; Invariant (physics); Curse of dimensionality; Feature extraction; Mathematics; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003335391,0.0005343665,0.0007491297,0.0009963984,0.0003951561,0.0008686408,0.000705733,0.0009420656,0.005860878],"category_scores_gemma":[0.0006314546,0.0003801209,0.0007539202,0.0007914052,0.0002416638,0.0007585822,0.0004296049,0.0006225414,0.004371295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002549007,"about_ca_system_score_gemma":0.0005221561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0021418,"about_ca_topic_score_gemma":0.003506876,"domain_scores_codex":[0.99965,0.00004228003,0.00002230048,0.00006885665,0.0001788738,0.00003770889],"domain_scores_gemma":[0.9997004,0.00006240103,0.00001723581,0.00006450329,0.0001402527,0.00001512976],"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":[0.0002388046,0.0001713633,0.0004188633,0.0001161661,0.0001080686,0.000154258,0.00003229847,0.00955304,0.2979491,0.003524643,0.003367864,0.6843656],"study_design_scores_gemma":[0.00005561028,0.0006349104,0.005209208,0.000050881,0.0003210939,0.00162925,0.00005934737,0.7224721,0.2386418,0.002841206,0.02797678,0.000107745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006682633,0.000561376,0.9897838,0.00007013471,0.0001755879,0.00005284598,0.00008163764,0.00100388,0.001588022],"genre_scores_gemma":[0.1069791,0.00146106,0.8713279,0.0002492933,0.0002228362,0.0001356447,0.0005037712,0.0001294044,0.01899094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005860878,"threshold_uncertainty_score":0.01960653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1045631969782427,"score_gpt":0.2586738773583835,"score_spread":0.1541106803801408,"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."}}