{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081551,0.0007023863,0.001311223,0.0009474742,0.0005917549,0.001024937,0.001074741,0.001204792,0.001923693],"category_scores_gemma":[0.001609828,0.0003235389,0.0008592669,0.001501287,0.0004490662,0.002407821,0.001715809,0.0007761054,0.001422872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005098089,"about_ca_system_score_gemma":0.0005241125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001324251,"about_ca_topic_score_gemma":0.0009064332,"domain_scores_codex":[0.9980232,0.0002660407,0.0001191369,0.0005598334,0.0008954021,0.0001365109],"domain_scores_gemma":[0.9994953,0.00005971082,0.00006984943,0.0001212632,0.0002196474,0.00003437128],"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":[0.0006888186,0.0001893205,0.002818878,0.0002114074,0.0001877313,0.0003301436,0.0001962903,0.04788865,0.3413672,0.009568222,0.002507448,0.5940458],"study_design_scores_gemma":[0.00004469726,0.0008606272,0.007771528,0.00005670624,0.0001685818,0.001336264,0.00006105255,0.8268072,0.1485137,0.0052189,0.008969199,0.000191592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04466219,0.0006130814,0.948488,0.0001597434,0.0001538071,0.0001386823,0.0001310131,0.002264227,0.003389254],"genre_scores_gemma":[0.6591112,0.0004196969,0.3347121,0.0001893567,0.0001011644,0.0001602437,0.0002622755,0.00005602816,0.004987895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001923693,"threshold_uncertainty_score":0.006435335,"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."}}