{"id":"W4413770722","doi":"10.1016/j.inffus.2025.103661","title":"Synthetic generation of finger-vein region by feature fusion-based enhanced U-transformer for finger-vein recognition","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; Ministry of Science and ICT, South Korea","keywords":"Computer science; Transformer; Fusion; Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Vein; Medicine; Engineering; Electrical engineering; Surgery; Voltage","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002383473,0.0002402795,0.0002440103,0.0002426529,0.0000878953,0.0002178246,0.0003309213,0.0002157039,0.000847546],"category_scores_gemma":[0.0005602105,0.0001015834,0.0003307636,0.0002291464,0.0001666032,0.0004072913,0.0002868773,0.0002075452,0.0002492396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001288219,"about_ca_system_score_gemma":0.0001729676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004390409,"about_ca_topic_score_gemma":0.0005697656,"domain_scores_codex":[0.9998577,0.00001889591,0.00000887033,0.00003323148,0.00006469543,0.00001661153],"domain_scores_gemma":[0.9998221,0.00004564162,0.00002538176,0.00004575458,0.00005177805,0.000009482147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004311309,0.00009377462,0.001705684,0.0001178474,0.0000370043,0.0002515112,0.00009045133,0.05133185,0.5437699,0.002783363,0.001229797,0.3981577],"study_design_scores_gemma":[0.00001496739,0.0002675683,0.001933788,0.000005279882,0.00003200662,0.0007272798,0.00002744842,0.7152472,0.2790062,0.0006746352,0.002044216,0.00001954299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1233274,0.0001596085,0.874099,0.00004124771,0.00004472608,0.00004682821,0.00006595747,0.0007290923,0.001486205],"genre_scores_gemma":[0.7697979,0.0001279654,0.22784,0.00004821831,0.00001372268,0.00002465072,0.0001756605,0.0000560441,0.001915849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000847546,"threshold_uncertainty_score":0.002835333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02130937938655922,"score_gpt":0.2455075897649463,"score_spread":0.2241982103783871,"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."}}