{"id":"W2975485695","doi":"10.1109/tcds.2019.2920364","title":"Deep Residual Network With Adaptive Learning Framework for Fingerprint Liveness Detection","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Liveness; Artificial intelligence; Spoofing attack; Convolutional neural network; Fingerprint (computing); Pattern recognition (psychology); Feature extraction; Deep learning; Residual; Fingerprint recognition; Artificial neural network; Multilayer perceptron; Feature (linguistics); Machine learning; Algorithm; Computer security","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":[0.0004739776,0.0007740128,0.0007055434,0.0004650625,0.000183449,0.0005258371,0.001382338,0.0007442166,0.001540446],"category_scores_gemma":[0.000879225,0.0003576869,0.0005748396,0.0004261142,0.0003287641,0.0007778017,0.0006251569,0.001121314,0.0004291506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006098946,"about_ca_system_score_gemma":0.0007517841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0105371,"about_ca_topic_score_gemma":0.008822281,"domain_scores_codex":[0.99978,0.0000350128,0.00001193363,0.00006961599,0.00005560209,0.00004777328],"domain_scores_gemma":[0.9998373,0.00004546879,0.00002323781,0.00001489224,0.0000674845,0.00001166457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002270734,0.000134922,0.00195239,0.0001471065,0.0001281265,0.0001914668,0.0000786216,0.6265954,0.01663904,0.007277057,0.003874579,0.3427541],"study_design_scores_gemma":[0.000003235556,0.00002349621,0.0001206522,0.000003582155,0.000008939419,0.00001352361,0.000003490013,0.9977634,0.000943594,0.0007440427,0.0003676297,0.000004419934],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03587867,0.002738592,0.9562283,0.0003592037,0.00009846813,0.00004422821,0.0001635234,0.00148608,0.003002877],"genre_scores_gemma":[0.8203135,0.0019391,0.1648668,0.0003786025,0.0001133203,0.0001482624,0.0007553445,0.0001278757,0.01135728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0105371,"threshold_uncertainty_score":0.02095151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163336013942574,"score_gpt":0.2358166226595319,"score_spread":0.2141832625201062,"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."}}