{"id":"W2909129969","doi":"10.3166/ts.35.341-354","title":"An improved fingerprint image matching and multi-view fingerprint recognition algorithm","year":2018,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Fingerprint (computing); Pattern recognition (psychology); Artificial intelligence; Matching (statistics); Fingerprint recognition; Computer science; Computer vision; Image (mathematics); Algorithm; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004794732,0.0005695096,0.001594709,0.001436437,0.0004291993,0.0008482562,0.001720607,0.001185636,0.003534547],"category_scores_gemma":[0.00106176,0.0004679818,0.001089171,0.001495773,0.0001856646,0.001385151,0.001030474,0.0009604356,0.002253989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003724053,"about_ca_system_score_gemma":0.0007994898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002481528,"about_ca_topic_score_gemma":0.002817082,"domain_scores_codex":[0.9988696,0.00009166333,0.00006051549,0.0002595153,0.0006169301,0.0001017358],"domain_scores_gemma":[0.9993671,0.00008260333,0.0000435814,0.0001610237,0.0003050475,0.00004063387],"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":[0.0003495836,0.0001765897,0.001185732,0.00009801505,0.0001243745,0.0001313479,0.00002863073,0.01003961,0.1510301,0.002090008,0.003470721,0.8312753],"study_design_scores_gemma":[0.00008770019,0.0003984099,0.006563003,0.00001729966,0.0001975354,0.002418521,0.00002726418,0.8635158,0.113602,0.001186211,0.01188867,0.00009757591],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01874807,0.0008418467,0.977159,0.0001006281,0.0001894714,0.00006881585,0.00007858793,0.001396471,0.001417113],"genre_scores_gemma":[0.1423617,0.0005435368,0.8475624,0.0001904169,0.000130659,0.000100211,0.0004312494,0.0001228907,0.008557036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003534547,"threshold_uncertainty_score":0.01182425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02879592402194851,"score_gpt":0.275909970526488,"score_spread":0.2471140465045395,"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."}}