{"id":"W4246395638","doi":"10.32920/ryerson.14646801","title":"Sequential subspace estimator for an efficient multibiometrics authentication and encryption","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Biometrics; Subspace topology; Encryption; Authentication (law); Data mining; Cryptography; Noise (video); Fingerprint (computing); Pattern recognition (psychology); Computer security; Algorithm; Artificial intelligence; Image (mathematics)","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":[0.001155502,0.0005928586,0.0007456426,0.0005211788,0.0003616045,0.0006811948,0.0006528572,0.0007611971,0.002837415],"category_scores_gemma":[0.002469407,0.0002963496,0.0006724899,0.0006377955,0.0004665137,0.001325304,0.0007815806,0.0009259921,0.001309777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004241558,"about_ca_system_score_gemma":0.0009210188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001221967,"about_ca_topic_score_gemma":0.001528383,"domain_scores_codex":[0.9989786,0.000250893,0.00005986527,0.000192596,0.0004608238,0.00005717202],"domain_scores_gemma":[0.9993002,0.0002421141,0.00008071397,0.0001277305,0.0002216471,0.00002740342],"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":[0.0004055641,0.0001419406,0.001763145,0.0002230919,0.0001281359,0.0001437343,0.0001434358,0.1213561,0.1073385,0.05488038,0.003760262,0.7097156],"study_design_scores_gemma":[0.00001682986,0.0001314238,0.0006217043,0.00001570237,0.00001365455,0.0002059501,0.00002036743,0.9666672,0.02101222,0.004418568,0.006849158,0.00002714918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003019018,0.0001887562,0.9962164,0.00005524636,0.00002965951,0.0000201196,0.00001855749,0.0001001029,0.0003521715],"genre_scores_gemma":[0.1205654,0.0005232471,0.8738769,0.0001071928,0.00006982238,0.0001218592,0.000209556,0.00004127023,0.004484754],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002837415,"threshold_uncertainty_score":0.009492099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0719615744055088,"score_gpt":0.3346970098399241,"score_spread":0.2627354354344154,"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."}}