{"id":"W2944648082","doi":"10.1109/access.2019.2914992","title":"A Multi-Biometric System Based on Feature and Score Level Fusions","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Biometrics; Weighting; Computer science; Pattern recognition (psychology); Artificial intelligence; Fusion; Feature (linguistics); Modalities; Modality (human–computer interaction); Sensor fusion; Fingerprint recognition; Fingerprint (computing)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002634094,0.0001173994,0.0001473089,0.001284843,0.0001264211,0.0005545888,0.001065126,0.0001050102,0.00001416266],"category_scores_gemma":[0.00004258433,0.00009765896,0.00004532154,0.004451906,0.00002329644,0.0004153298,0.0001463993,0.0001442061,0.0001604295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005563077,"about_ca_system_score_gemma":0.00005245182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006126345,"about_ca_topic_score_gemma":0.000008444391,"domain_scores_codex":[0.9988437,0.00005452523,0.0001368177,0.0004391943,0.000338452,0.0001873203],"domain_scores_gemma":[0.998898,0.0001224681,0.0000913015,0.0006616687,0.0001143107,0.0001121924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001518108,0.003827815,0.3575911,0.003786194,0.0002404311,0.0002615377,0.002992431,0.0008119295,0.05083477,0.04867039,0.09591286,0.4349188],"study_design_scores_gemma":[0.001394683,0.00007085075,0.4951074,0.000116359,0.000009628093,0.0000124049,0.00003182526,0.4924016,0.004223822,0.00002615042,0.006236719,0.0003685538],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4350553,0.0002764222,0.557423,0.001417279,0.003327688,0.0007032029,0.00005961439,0.0003635713,0.001373901],"genre_scores_gemma":[0.9935047,0.000005602581,0.005083106,0.0004141016,0.00003423953,0.00001144588,0.0000051766,0.000006683616,0.0009349303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5584494,"threshold_uncertainty_score":0.5347913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0899867567989089,"score_gpt":0.3107564861189591,"score_spread":0.2207697293200502,"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."}}