{"id":"W2119933351","doi":"10.1109/ipta.2008.4743776","title":"Score Fusion of SVD and DCT-RLDA for Face Recognition","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Normalization (sociology); Discrete cosine transform; Singular value decomposition; Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Computer science; Linear discriminant analysis; Biometrics; Fusion; Feature extraction; Face (sociological concept); Fusion rules; Mathematics; Image fusion; Image (mathematics)","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":[0.001633877,0.0006442371,0.0009276749,0.001372636,0.0003152818,0.0009075972,0.000727997,0.0004834189,0.002104848],"category_scores_gemma":[0.002984717,0.0002365056,0.0009072164,0.001298321,0.0004258937,0.0009319609,0.001016493,0.0005869184,0.001754543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003560603,"about_ca_system_score_gemma":0.00055952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001321576,"about_ca_topic_score_gemma":0.001514843,"domain_scores_codex":[0.9983225,0.0003441413,0.00009413587,0.0002416882,0.0008999436,0.00009755962],"domain_scores_gemma":[0.9991367,0.0001838913,0.00005853032,0.0001519795,0.0004221524,0.00004684145],"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":[0.0005469194,0.0001124004,0.001333384,0.0001598188,0.0001236261,0.00008055943,0.00007252213,0.02750502,0.09225756,0.009930071,0.001876917,0.8660012],"study_design_scores_gemma":[0.00003199094,0.0005012823,0.003624793,0.00002521465,0.00009816143,0.0004861376,0.00006563027,0.8845882,0.09675802,0.007230857,0.006503208,0.00008643183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0210955,0.0004781409,0.9760622,0.00007154288,0.00008985544,0.0000602101,0.00007845003,0.000677753,0.001386368],"genre_scores_gemma":[0.3547021,0.0005554729,0.6405144,0.00006542358,0.0001185075,0.0001182627,0.0005465555,0.00008356112,0.003295636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002104848,"threshold_uncertainty_score":0.008640885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06127177345998677,"score_gpt":0.2466527304343343,"score_spread":0.1853809569743475,"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."}}