{"id":"W2179756850","doi":"10.4018/ijssci.2015010101","title":"Feature and Rank Level Fusion for Privacy Preserved Multi-Biometric System","year":2015,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Biometrics; Random projection; Feature (linguistics); Authentication (law); Artificial intelligence; Face (sociological concept); Projection (relational algebra); Pattern recognition (psychology); Facial recognition system; Template; Data mining; Computer security; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002214575,0.0001245793,0.0001709208,0.001446387,0.0001946804,0.0006969751,0.00165952,0.00005819935,0.000001430317],"category_scores_gemma":[0.002336642,0.0001047655,0.00005728023,0.00179449,0.0002979141,0.001497213,0.0003990878,0.0001389733,0.000004116419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002077312,"about_ca_system_score_gemma":0.0006566631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001546659,"about_ca_topic_score_gemma":0.000001215716,"domain_scores_codex":[0.9975015,0.00004023305,0.0004110023,0.0002970984,0.001569312,0.000180846],"domain_scores_gemma":[0.9925374,0.0003903407,0.0004065896,0.000141578,0.006244141,0.0002799154],"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":[0.0002500083,0.0005502959,0.009339373,0.0001449735,0.0001781183,0.000064217,0.005401383,0.006904576,0.0007733342,0.1594298,0.005333034,0.811631],"study_design_scores_gemma":[0.002311868,0.0005951029,0.0552901,0.0003077892,0.00003158032,0.001849464,0.001597237,0.8893349,0.003283286,0.02859298,0.0161712,0.0006345435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02049442,0.000798821,0.9748763,0.002268544,0.001335033,0.0001569299,0.00002219882,0.00003328023,0.00001443749],"genre_scores_gemma":[0.664355,0.0000432053,0.3352388,0.000179456,0.00008192923,0.000004091783,0.000003837337,0.000003900141,0.0000898529],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8824303,"threshold_uncertainty_score":0.6720947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1228031871174421,"score_gpt":0.3503421812595449,"score_spread":0.2275389941421028,"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."}}