{"id":"W4206100981","doi":"10.1142/s0219467823500195","title":"Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks","year":2021,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Feature (linguistics); Identification (biology); Convolutional neural network; Face (sociological concept); Feature extraction; Pattern recognition (psychology); Modality (human–computer interaction); Authentication (law); Facial recognition system; Convolution (computer science); Artificial neural network; Computer vision; Computer security","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":[0.0003831256,0.0005118262,0.0007147112,0.0005377569,0.0002604237,0.0004148495,0.0004475549,0.000542232,0.003033664],"category_scores_gemma":[0.0006051888,0.0001534869,0.000579097,0.0004481609,0.0002285171,0.0008417924,0.0005443453,0.0004168303,0.001016108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004832934,"about_ca_system_score_gemma":0.000327284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00271783,"about_ca_topic_score_gemma":0.003409223,"domain_scores_codex":[0.999638,0.0000427273,0.00002115586,0.0001018516,0.0001441154,0.00005215245],"domain_scores_gemma":[0.9998374,0.00002874726,0.00002789032,0.00002425664,0.00007082933,0.00001093375],"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":[0.001125314,0.000262272,0.00715266,0.0002457885,0.0001998677,0.0004973619,0.00009310148,0.04596754,0.1534067,0.002554779,0.00548239,0.7830122],"study_design_scores_gemma":[0.00002178118,0.0004183967,0.01343892,0.00004657854,0.0001352136,0.001467292,0.00005597522,0.8788824,0.09936434,0.001769742,0.004337799,0.00006159025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2460508,0.003036341,0.7351258,0.0005245379,0.0003826013,0.0001673757,0.0008112959,0.004517069,0.009384145],"genre_scores_gemma":[0.8714129,0.001028129,0.1170423,0.0001763353,0.00006589296,0.0000677979,0.000786607,0.00004764258,0.009372279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003033664,"threshold_uncertainty_score":0.01014864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02852861381814475,"score_gpt":0.2768970122431295,"score_spread":0.2483683984249847,"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."}}