{"id":"W2996857901","doi":"","title":"Fast Minutia-based Palmprint Matching Using CNN and Generalized Hough Transform","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Minutiae; Artificial intelligence; Hough transform; Convolutional neural network; Computer science; Matching (statistics); Computer vision; Pattern recognition (psychology); Rotation (mathematics); Image (mathematics); Process (computing); Mathematics; Fingerprint recognition; Fingerprint (computing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008070424,0.0001099332,0.0002375217,0.0004438132,0.000115741,0.0005674777,0.0002025877,0.0000302569,0.000005593757],"category_scores_gemma":[0.00001251929,0.00008859988,0.00007042848,0.0003215842,0.00003319579,0.0005886657,0.00003905265,0.0001269355,0.000003913343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004699052,"about_ca_system_score_gemma":0.00009906148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002922594,"about_ca_topic_score_gemma":1.967032e-7,"domain_scores_codex":[0.9986069,0.0001032526,0.0005280178,0.0001603221,0.0004801222,0.0001213977],"domain_scores_gemma":[0.999,0.0001398336,0.0003548744,0.0001064498,0.0002889633,0.0001098403],"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":[0.000250978,0.0007582943,0.02008029,0.001523852,0.0003216907,0.0001268717,0.01176062,0.4237932,0.02851553,0.06750255,0.00223807,0.443128],"study_design_scores_gemma":[0.001240422,0.00004166838,0.01142154,0.0001740638,0.000009061374,0.0003853234,0.0002020478,0.9823739,0.0001347528,0.002141378,0.001743921,0.0001318942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3394693,0.000753206,0.6582812,0.0007526891,0.0005913664,0.00009010527,0.000001654744,0.00001265663,0.00004788037],"genre_scores_gemma":[0.9644578,0.00001327526,0.03521834,0.0002053606,0.00006148296,4.236122e-7,0.000001807421,0.000005862335,0.00003572642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6249885,"threshold_uncertainty_score":0.5472201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376201525712575,"score_gpt":0.2739707817555335,"score_spread":0.2602087664984077,"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."}}