{"id":"W2079716275","doi":"10.1109/cw.2011.48","title":"A Novel Multi-modal Biometric Architecture for High-Dimensional Features","year":2011,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Linear subspace; Cluster analysis; Artificial intelligence; Modal; Dimensionality reduction; Pattern recognition (psychology); Feature vector; Subspace topology; Facial recognition system; Curse of dimensionality; Feature (linguistics); Face (sociological concept); Set (abstract data type); Data set; Data mining; Machine learning; 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":[0.0004746122,0.0003332057,0.0004352074,0.0004890329,0.0004223462,0.0005680266,0.001108505,0.0009057252,0.004264537],"category_scores_gemma":[0.0007136315,0.0002110687,0.0004308747,0.0006277903,0.0003204516,0.001342111,0.0009245917,0.0006696768,0.001929417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003265509,"about_ca_system_score_gemma":0.0003122328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009614071,"about_ca_topic_score_gemma":0.001637757,"domain_scores_codex":[0.999542,0.00007632287,0.00002108998,0.0001177709,0.0002056136,0.00003715819],"domain_scores_gemma":[0.9997529,0.0000358312,0.00002183864,0.00005716168,0.0001132431,0.00001900717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002660535,0.0001473791,0.0008764788,0.0001961849,0.00007244382,0.000207328,0.0001705553,0.02316916,0.3976798,0.01884964,0.005478808,0.5528862],"study_design_scores_gemma":[0.00003436582,0.0004814512,0.003072678,0.00004894249,0.00006232343,0.001317539,0.00006120034,0.8640796,0.0922147,0.01071515,0.02782276,0.0000892501],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01052708,0.0002385702,0.985999,0.0001237021,0.00008064359,0.00004794023,0.00007437771,0.001019804,0.001888893],"genre_scores_gemma":[0.3089418,0.0004449695,0.6798282,0.0003285057,0.0001147997,0.0002502798,0.0003208169,0.0000679204,0.009702588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004264537,"threshold_uncertainty_score":0.01426631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05642416682214457,"score_gpt":0.2654269463880528,"score_spread":0.2090027795659082,"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."}}