{"id":"W2076707216","doi":"10.1142/s0219467814500211","title":"Fingerprint Liveness Detection Using Multiple Static Features and Random Forests","year":2014,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; University of Calgary","keywords":"Liveness; Fingerprint (computing); Computer science; Artificial intelligence; Pattern recognition (psychology); Spoofing attack; Random forest; Classifier (UML); Fingerprint recognition; Biometrics; Fingerprint Verification Competition; Noise (video); Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001587223,0.0008952611,0.001251324,0.003310421,0.000448315,0.0006677843,0.0009342386,0.0009826907,0.0006010566],"category_scores_gemma":[0.002469962,0.0003831277,0.001195548,0.001399703,0.0003875788,0.001263851,0.0005904917,0.0005583639,0.0005163958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00030488,"about_ca_system_score_gemma":0.0004018728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00296263,"about_ca_topic_score_gemma":0.002859889,"domain_scores_codex":[0.9987856,0.0001950468,0.00007478459,0.0003066346,0.0004551964,0.0001828795],"domain_scores_gemma":[0.9985544,0.0005937847,0.0002404571,0.0001588731,0.0003870095,0.00006548141],"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":[0.000583226,0.0002870476,0.008347678,0.0001495218,0.0001945683,0.0003372911,0.00008476801,0.09205163,0.07157638,0.001148788,0.001465124,0.823774],"study_design_scores_gemma":[0.00001197911,0.0001368022,0.006023515,0.00001582614,0.00006583855,0.0004124815,0.00002970515,0.9710231,0.02033128,0.001165795,0.000748816,0.00003502713],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1154263,0.0008635426,0.8810254,0.00007706315,0.00004442575,0.00007090683,0.0001299652,0.001656507,0.0007060253],"genre_scores_gemma":[0.7603247,0.0004516274,0.2372988,0.00004984214,0.00008714936,0.00005982175,0.0004590063,0.00008158383,0.001187329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003310421,"threshold_uncertainty_score":0.008394182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0135671456756219,"score_gpt":0.2714141691401842,"score_spread":0.2578470234645623,"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."}}