{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002037515,0.0001414877,0.0001801472,0.002084617,0.0003626033,0.0005432931,0.0006010013,0.00005184147,0.00001288297],"category_scores_gemma":[0.0003656434,0.0001417499,0.00005652567,0.009774018,0.00006399798,0.0005702238,0.0004110218,0.0001338191,0.0001354552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007849826,"about_ca_system_score_gemma":0.00005882873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001274484,"about_ca_topic_score_gemma":0.000001192326,"domain_scores_codex":[0.9978585,0.0001189795,0.0003881851,0.0007039008,0.0005364798,0.0003939482],"domain_scores_gemma":[0.9988019,0.0002156205,0.0001340813,0.0005778226,0.0001316717,0.000138869],"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":[0.00001334749,0.001683964,0.007045864,0.001043197,0.0001938952,0.0000265211,0.02921658,0.0001884317,0.1755074,0.3061412,0.002892163,0.4760475],"study_design_scores_gemma":[0.0003095422,0.00001983052,0.03109861,0.00001671484,0.00001135403,0.00001526267,0.0005214827,0.9453917,0.01712666,0.004479551,0.0006709308,0.0003383498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2592713,0.00007045694,0.7379658,0.0008598863,0.000166802,0.0001614515,0.000002088772,0.0005504275,0.0009518356],"genre_scores_gemma":[0.9677749,0.00002658544,0.03150071,0.000119757,0.00003358992,0.00002520279,0.00001734168,0.00000899729,0.0004929434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9452033,"threshold_uncertainty_score":0.5780393,"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."}}