{"id":"W1535203525","doi":"10.1007/11608288_65","title":"Iris Recognition with Support Vector Machines","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Artificial intelligence; Computer science; Iris recognition; Support vector machine; Pattern recognition (psychology); Thresholding; Eyelash; Hough transform; IRIS (biosensor); Canny edge detector; Computer vision; Classifier (UML); Artificial neural network; Edge detection; Biometrics; Image processing; Image (mathematics)","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":[0.0004184419,0.0005336442,0.0006435031,0.0006814477,0.000166598,0.0008232265,0.0006060015,0.0005837571,0.01009174],"category_scores_gemma":[0.001289188,0.0004023291,0.0005252701,0.0009018501,0.0002050853,0.001086259,0.0007630956,0.0007128138,0.007070791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233391,"about_ca_system_score_gemma":0.0001641566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003805911,"about_ca_topic_score_gemma":0.000590838,"domain_scores_codex":[0.9995049,0.00009061077,0.00003394242,0.00009908398,0.00024065,0.00003068964],"domain_scores_gemma":[0.9997094,0.0001069016,0.00002944894,0.00007567851,0.00007034034,0.000008201576],"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":[0.00009763672,0.00003075509,0.0002950978,0.0001499342,0.0000415091,0.00004233365,0.00002153139,0.008499767,0.02374751,0.002666625,0.005759642,0.9586477],"study_design_scores_gemma":[0.00002955467,0.0002880306,0.003245839,0.0001444171,0.0001014514,0.001052577,0.00006546293,0.7898824,0.1309087,0.0122286,0.06197416,0.00007873342],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006084201,0.0023512,0.9816315,0.0001298732,0.0001765717,0.00004855146,0.0001535737,0.00289068,0.006533894],"genre_scores_gemma":[0.1752959,0.00305123,0.7910891,0.0001600612,0.0002270728,0.0001385854,0.0006750303,0.0002834841,0.02907951],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01009174,"threshold_uncertainty_score":0.03376025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317202760250498,"score_gpt":0.246878880862528,"score_spread":0.223706853260023,"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."}}