{"id":"W2797298759","doi":"10.1007/s00500-018-3182-1","title":"Difference co-occurrence matrix using BP neural network for fingerprint liveness detection","year":2018,"lang":"en","type":"article","venue":"Soft Computing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Liveness; Computer science; Artificial neural network; Artificial intelligence; Preprocessor; Fingerprint (computing); Pattern recognition (psychology); Fingerprint recognition; Spoofing attack; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006236777,0.0001453522,0.0001678453,0.0001646927,0.0007194069,0.0003563837,0.0007088459,0.00007607273,0.000004363837],"category_scores_gemma":[0.0001445962,0.0001508564,0.00008062648,0.00106564,0.00008935363,0.0001749549,0.0002598959,0.0001401815,0.0000186881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006926847,"about_ca_system_score_gemma":0.00005892537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004439091,"about_ca_topic_score_gemma":0.000009922058,"domain_scores_codex":[0.9984813,0.00008796905,0.000307189,0.000486642,0.000217553,0.0004193019],"domain_scores_gemma":[0.9987347,0.0003171028,0.0002170562,0.0003875144,0.0002566622,0.00008703906],"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":[0.00003334683,0.0001294696,0.009625178,0.0001586129,0.00003524827,0.000005080772,0.00224927,0.005360885,0.007013146,0.006756854,0.0005041111,0.9681288],"study_design_scores_gemma":[0.0001609699,0.0000542153,0.009599178,0.00003234259,0.000005390092,0.00001877236,0.00001268052,0.9856632,0.002001358,0.001012351,0.001245422,0.0001941242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2684812,0.00006657176,0.7293769,0.00004220796,0.001665519,0.0001646625,0.000005338092,0.0001840201,0.00001359424],"genre_scores_gemma":[0.9514241,9.450506e-7,0.047859,0.0001326512,0.0005516954,0.000003777059,0.000006536594,0.000006460021,0.00001485992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9803023,"threshold_uncertainty_score":0.6151747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05099264613434697,"score_gpt":0.3298064939482945,"score_spread":0.2788138478139475,"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."}}