{"id":"W2944214248","doi":"10.2478/jaiscr-2019-0001","title":"Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System","year":2019,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Soft Computing Research","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Mitacs","keywords":"Biometrics; Artificial intelligence; Gait; Computer science; Pattern recognition (psychology); Feature (linguistics); Modality (human–computer interaction); Rank (graph theory); Modal; Face (sociological concept); Support vector machine; Logistic regression; Machine learning; Mathematics; Medicine; Physical medicine and rehabilitation","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":[0.000690707,0.0007029491,0.000790882,0.001354451,0.000268369,0.0008353286,0.0005036079,0.0005998233,0.002981436],"category_scores_gemma":[0.001546587,0.0001766877,0.0004804129,0.0007193965,0.000199464,0.0007111263,0.0007779729,0.0004305378,0.001072917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004228199,"about_ca_system_score_gemma":0.0003589764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001802935,"about_ca_topic_score_gemma":0.002055395,"domain_scores_codex":[0.9987887,0.0001532657,0.00008660428,0.0001820589,0.000683769,0.0001056517],"domain_scores_gemma":[0.9994936,0.0000495449,0.00007247688,0.00005429039,0.0002919782,0.00003803938],"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":[0.001929036,0.0003603475,0.01500566,0.000507644,0.0002287934,0.0004200132,0.0001395413,0.03938898,0.2037809,0.002805941,0.007016265,0.7284169],"study_design_scores_gemma":[0.00004538777,0.0007045598,0.04957531,0.00009607747,0.00009862141,0.000853855,0.000168285,0.8185382,0.1217354,0.002610583,0.005407877,0.0001658562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2490914,0.002033386,0.7318155,0.0004509781,0.0004565783,0.0002709994,0.002264982,0.00480532,0.008810897],"genre_scores_gemma":[0.8884118,0.0003810946,0.106171,0.0001055969,0.00005035468,0.0001287039,0.001130333,0.00005026322,0.003571015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002981436,"threshold_uncertainty_score":0.009973884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2839608259157278,"score_gpt":0.3832731189089257,"score_spread":0.09931229299319794,"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."}}