{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003961363,0.0003749756,0.0004727695,0.000941181,0.000276694,0.0003948535,0.0004690007,0.0004277071,0.001642272],"category_scores_gemma":[0.001205284,0.000145597,0.0003220322,0.00106812,0.0001761976,0.0005059548,0.000345483,0.0005428991,0.0004506538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002576647,"about_ca_system_score_gemma":0.0004500731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005245811,"about_ca_topic_score_gemma":0.005504325,"domain_scores_codex":[0.9996258,0.00003885082,0.0000223256,0.0001133141,0.0001534699,0.00004621484],"domain_scores_gemma":[0.9994299,0.0001856495,0.00004245182,0.00004182704,0.000271111,0.00002898123],"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":[0.0009795938,0.0003704068,0.009622614,0.0002037578,0.0001017558,0.0002380317,0.00009574286,0.03354636,0.08923287,0.00128787,0.002369971,0.861951],"study_design_scores_gemma":[0.00001007236,0.0000908019,0.01079739,0.000009811244,0.00003281709,0.0002326367,0.00003191759,0.9667584,0.02075308,0.0004222763,0.0008380777,0.00002263776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2400824,0.0008645121,0.7546967,0.0001679344,0.0002757561,0.0000891648,0.0004368976,0.001140239,0.002246411],"genre_scores_gemma":[0.8661535,0.0003489303,0.1294675,0.0000430658,0.00005803013,0.00006676953,0.0003864475,0.0000417784,0.003433908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005245811,"threshold_uncertainty_score":0.01043051,"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."}}