{"id":"W4402362360","doi":"10.62036/isd.2024.54","title":"Finger Vein Presentation Attack Detection Method Using a Hybridized Gray-Level Co-Occurrence Matrix Feature with Light-Gradient Boosting Machine Model","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Information Systems Development","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Computer science; Gray level; Gradient boosting; Co-occurrence matrix; Pattern recognition (psychology); Boosting (machine learning); Computer vision; Feature extraction; Gray (unit); Pixel; Image processing; Image (mathematics); Radiology; Medicine; Image texture","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":[0.0005890724,0.000337643,0.0006803281,0.0008049157,0.0002385633,0.0004735209,0.0005617044,0.0004658226,0.0008450811],"category_scores_gemma":[0.0009012187,0.0001599653,0.0006236525,0.0004559996,0.0002174179,0.0004480292,0.0003191269,0.0004830702,0.0004342385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003977053,"about_ca_system_score_gemma":0.0004680371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002210378,"about_ca_topic_score_gemma":0.001865333,"domain_scores_codex":[0.9996789,0.00005429977,0.00001489762,0.00006774034,0.0001356416,0.00004855735],"domain_scores_gemma":[0.9996841,0.00007428708,0.00003639434,0.0000295155,0.0001510196,0.00002475566],"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":[0.0004895636,0.0004258059,0.01238909,0.0001152173,0.0001632555,0.0002354714,0.0001033624,0.1752068,0.07785629,0.003632369,0.004461937,0.7249209],"study_design_scores_gemma":[0.00000567395,0.00008120658,0.00214403,0.000004089058,0.00001877678,0.00009608023,0.000007541585,0.9893749,0.007147451,0.0004501448,0.0006606499,0.000009510618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.129964,0.0004651877,0.8659005,0.0002303467,0.0001073851,0.0001102243,0.00007831819,0.001106929,0.002037063],"genre_scores_gemma":[0.8763182,0.0002194309,0.120249,0.0001280664,0.00005663656,0.00006488129,0.0001530357,0.00003650049,0.002774221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002210378,"threshold_uncertainty_score":0.004395008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0738367536876496,"score_gpt":0.3314229386426782,"score_spread":0.2575861849550287,"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."}}