{"id":"W4405303824","doi":"10.1109/tbiom.2024.3516634","title":"A Deep CNN-Based Feature Extraction and Matching of Pores for Fingerprint Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Biometrics Behavior and Identity Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fingerprint (computing); Pattern recognition (psychology); Artificial intelligence; Matching (statistics); Feature extraction; Computer science; Feature (linguistics); Extraction (chemistry); Mathematics; Chemistry; Chromatography; Statistics; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002547083,0.0005868624,0.0005611805,0.0006163077,0.0002529588,0.0005307756,0.001387087,0.000738482,0.002677505],"category_scores_gemma":[0.0006112931,0.0003715019,0.0007111941,0.000653457,0.0003356761,0.000982153,0.0008009814,0.0006684918,0.0009437366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009249053,"about_ca_system_score_gemma":0.0009280334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007921965,"about_ca_topic_score_gemma":0.007894027,"domain_scores_codex":[0.9997213,0.00001239324,0.00001211357,0.00008656545,0.0001074976,0.00005999205],"domain_scores_gemma":[0.9998704,0.00001706841,0.00001781203,0.00002755915,0.00005279821,0.00001432075],"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":[0.0002872728,0.0001694144,0.002052194,0.0001705052,0.0001174863,0.0003007903,0.00004960707,0.1040215,0.2496585,0.005202303,0.008450918,0.6295196],"study_design_scores_gemma":[0.00001047533,0.00009387244,0.001441702,0.00001389866,0.00003061111,0.0001958464,0.000008076555,0.9431621,0.05015058,0.001202744,0.003670596,0.00001947892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04224604,0.0005513276,0.9483028,0.0002201531,0.0001520728,0.0001199601,0.0005623905,0.004303492,0.00354171],"genre_scores_gemma":[0.5670026,0.0006370972,0.4172273,0.0003812984,0.00006548003,0.0001635371,0.001820341,0.0001645879,0.01253776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007921965,"threshold_uncertainty_score":0.01575172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04040861572360703,"score_gpt":0.3303098214017909,"score_spread":0.2899012056781838,"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."}}