{"id":"W3091775538","doi":"10.1109/tce.2020.3029955","title":"Mobile Match on Card Active Authentication Using Touchscreen Biometric","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Touchscreen; Computer science; Authentication (law); Mobile device; Login; Smart card; Biometrics; Overhead (engineering); Embedded system; Computer hardware; Computer security; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003956761,0.0004738611,0.0005344027,0.0004724611,0.0002969879,0.0006174848,0.0006022482,0.0007145263,0.003512019],"category_scores_gemma":[0.001226652,0.0001647462,0.0004394622,0.0004051122,0.0003100247,0.001417576,0.0009548563,0.0004104978,0.001574057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003315507,"about_ca_system_score_gemma":0.0003415696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001409343,"about_ca_topic_score_gemma":0.001354603,"domain_scores_codex":[0.9993349,0.0001200564,0.00005288404,0.0001366129,0.0002777815,0.00007771879],"domain_scores_gemma":[0.9994932,0.00006716694,0.00007659798,0.0002080916,0.0001210123,0.00003377417],"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":[0.001886626,0.0002810589,0.009300072,0.0002850243,0.000162784,0.0007468824,0.0001921239,0.01862261,0.1807547,0.007221404,0.006245482,0.7743013],"study_design_scores_gemma":[0.00009791915,0.001167228,0.01573662,0.00008495784,0.0001375197,0.002833996,0.0001013169,0.7980261,0.1597657,0.004721913,0.01719736,0.0001292621],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2232746,0.001246764,0.7557598,0.0003606322,0.0004380829,0.0003073104,0.0004165118,0.006465486,0.01173086],"genre_scores_gemma":[0.9550381,0.0002632785,0.03927,0.0001392374,0.0000304106,0.00003630841,0.0001752108,0.00002633507,0.005021157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003512019,"threshold_uncertainty_score":0.01174891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03578144127722082,"score_gpt":0.2735957283535536,"score_spread":0.2378142870763328,"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."}}