{"id":"W1504994924","doi":"10.1007/978-3-540-25948-0_103","title":"Feature-Level Fusion for Effective Palmprint Authentication","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Hamming distance; Fusion; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Biometrics; Code (set theory); Authentication (law); Gabor filter; Feature extraction; Computer vision; Algorithm","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":[0.0004079329,0.0004737845,0.0006834115,0.0005445939,0.0003000354,0.0006187582,0.0006021303,0.0007693832,0.004292113],"category_scores_gemma":[0.0009212273,0.0003059474,0.0005303968,0.0007911499,0.0003340823,0.001464262,0.000994332,0.000642039,0.001855994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002445373,"about_ca_system_score_gemma":0.0002373907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004109093,"about_ca_topic_score_gemma":0.0006679778,"domain_scores_codex":[0.9995638,0.00005827912,0.00002418335,0.00006881853,0.0002298024,0.0000550699],"domain_scores_gemma":[0.9997497,0.0000868768,0.00001956701,0.00006898893,0.00006552254,0.000009353302],"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":[0.0003656549,0.0001077496,0.0002713745,0.0001330076,0.00004820652,0.00007596728,0.00004747083,0.01888114,0.315107,0.00498121,0.002306464,0.6576747],"study_design_scores_gemma":[0.00002689675,0.0002372617,0.002187373,0.00003105419,0.00009843791,0.0005928116,0.00003502188,0.6066158,0.3744311,0.007449789,0.008236825,0.00005768715],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02019923,0.0008005027,0.9760066,0.00008037894,0.00006463657,0.00003306462,0.00008072033,0.001027529,0.001707257],"genre_scores_gemma":[0.4322413,0.00116069,0.5592288,0.0001380026,0.00007947075,0.00008358605,0.0003140151,0.000149996,0.006604176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004292113,"threshold_uncertainty_score":0.01435852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02538558841235674,"score_gpt":0.2673124641116527,"score_spread":0.241926875699296,"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."}}