{"id":"W3046431518","doi":"10.1007/978-3-030-54407-2_35","title":"Fingerprint Liveness Detection Based on Multi-modal Fine-Grained Feature Fusion","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Liveness; Computer science; Fingerprint (computing); Modal; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Fingerprint recognition; Fusion; Biometrics; Computer vision; Theoretical computer science","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":[0.0003677582,0.0004947386,0.000839162,0.001171812,0.0002379702,0.000604114,0.000643739,0.0007436196,0.00169659],"category_scores_gemma":[0.0008677023,0.0002413605,0.0004989057,0.0009373981,0.0003063175,0.001381726,0.0008957884,0.0005234987,0.0009641384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001800587,"about_ca_system_score_gemma":0.0001937046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003850389,"about_ca_topic_score_gemma":0.0006359,"domain_scores_codex":[0.9995906,0.00004610874,0.00001818694,0.0001175471,0.000171102,0.00005645629],"domain_scores_gemma":[0.999622,0.00008688692,0.00005953028,0.00008503201,0.0001220764,0.00002442945],"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":[0.0005069656,0.0001639532,0.003631577,0.0001796339,0.00007412217,0.0001622381,0.00006702769,0.009215273,0.4146135,0.001210586,0.001368285,0.5688069],"study_design_scores_gemma":[0.00003161555,0.0006504384,0.02835583,0.00005062105,0.0001967231,0.002124097,0.000112573,0.7409622,0.2194467,0.003998344,0.00392973,0.0001411112],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.101643,0.000900707,0.8931436,0.000138074,0.0001027193,0.00006685687,0.0002557934,0.00125231,0.002497038],"genre_scores_gemma":[0.7866772,0.0007032973,0.2085296,0.0001383467,0.00009119549,0.00005625554,0.0003921874,0.0000717162,0.003340345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00169659,"threshold_uncertainty_score":0.005675673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02298677429871035,"score_gpt":0.2477543719419877,"score_spread":0.2247675976432773,"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."}}