{"id":"W4397000326","doi":"10.1109/scc59637.2023.10527686","title":"Enhancing Biometric Authentication Efficiency: A Hybrid Approach Exploiting Iris Modality and Leveraging One-Class SVM","year":2023,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Biometrics; Iris recognition; Artificial intelligence; Convolutional neural network; Support vector machine; Feature extraction; Machine learning; Pattern recognition (psychology); Modalities; Data mining","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":[0.0009203742,0.0007653743,0.001029497,0.001075502,0.0003429374,0.0009082962,0.000737326,0.0007575325,0.00202004],"category_scores_gemma":[0.001415887,0.0001789331,0.0006606628,0.0007176477,0.0002320693,0.001214941,0.0008312362,0.0006584097,0.001699788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003077883,"about_ca_system_score_gemma":0.0004760371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001184251,"about_ca_topic_score_gemma":0.001507592,"domain_scores_codex":[0.9993976,0.000103592,0.00004580526,0.0001635426,0.0002096093,0.00007988221],"domain_scores_gemma":[0.9993297,0.0001431263,0.00007754617,0.0001393945,0.0002753932,0.00003481952],"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":[0.0006580802,0.0004807364,0.005607791,0.0001268954,0.0001375076,0.0001614029,0.00006210637,0.02169917,0.1101311,0.001342216,0.004108472,0.8554846],"study_design_scores_gemma":[0.00001874906,0.0002803024,0.00652822,0.00002072304,0.00007684571,0.0003561316,0.00004523613,0.9336506,0.05523495,0.0009860341,0.002762967,0.0000392789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1692953,0.001358461,0.8174512,0.0003918223,0.0003267173,0.000149045,0.0003107007,0.005083648,0.005633082],"genre_scores_gemma":[0.8425859,0.0004353311,0.1496717,0.0001729965,0.0001191608,0.00006483796,0.0005710364,0.00009277393,0.006286277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00202004,"threshold_uncertainty_score":0.006757677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05701446500888871,"score_gpt":0.2665079956918101,"score_spread":0.2094935306829214,"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."}}