{"id":"W3094276816","doi":"10.18280/ria.340402","title":"Improvements on Learning Kernel Extended Dictionary for Face Recognition","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Sparse approximation; Kernel (algebra); Pattern recognition (psychology); K-SVD; Face (sociological concept); Occlusion; Computer science; Representation (politics); Set (abstract data type); Facial recognition system; Image (mathematics); Dictionary learning; Mathematics; Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0007441211,0.0008270717,0.001421442,0.0009105764,0.0003905227,0.0008364844,0.001584695,0.0008931243,0.004343545],"category_scores_gemma":[0.002613828,0.0003849521,0.001167212,0.001096221,0.0004418403,0.002009465,0.001598783,0.001592261,0.00269081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005043689,"about_ca_system_score_gemma":0.0008836767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00717883,"about_ca_topic_score_gemma":0.005521342,"domain_scores_codex":[0.998962,0.000178681,0.0000639244,0.0002532623,0.0004457779,0.00009628633],"domain_scores_gemma":[0.9992064,0.0001842834,0.0000492474,0.0002218496,0.0003018769,0.0000365033],"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":[0.0002705347,0.0001702675,0.001286854,0.000178992,0.0001409962,0.0001289391,0.0001063399,0.1661272,0.01775139,0.01610237,0.0112126,0.7865236],"study_design_scores_gemma":[0.00001284477,0.0000394119,0.000184502,0.000005604117,0.00001061129,0.00008574619,0.00001297369,0.9914969,0.002934627,0.002774848,0.002429347,0.00001262363],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008908531,0.0005537951,0.9868508,0.0001401958,0.0000882021,0.00004365467,0.000137151,0.0017839,0.001493733],"genre_scores_gemma":[0.3144409,0.001357112,0.6694195,0.0005163684,0.0001997107,0.0002592531,0.002414665,0.0004610935,0.01093132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00717883,"threshold_uncertainty_score":0.0145306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0686789065295118,"score_gpt":0.2817971140075889,"score_spread":0.2131182074780771,"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."}}