{"id":"W2143304877","doi":"10.1109/tnn.2002.806629","title":"Face recognition using kernel direct discriminant analysis algorithms","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":608,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Kernel Fisher discriminant analysis; Linear discriminant analysis; Kernel principal component analysis; Pattern recognition (psychology); Facial recognition system; Kernel (algebra); Artificial intelligence; Computer science; Principal component analysis; Face (sociological concept); Discriminant; Kernel method; Feature extraction; Word error rate; Algorithm; Mathematics; Support vector machine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000184187,0.0002390584,0.0002724331,0.0002994937,0.0004019183,0.0001697338,0.0002643946,0.0001356344,0.00008850259],"category_scores_gemma":[0.000004672373,0.0002126087,0.0003127706,0.001443681,0.00003862516,0.0006074207,0.000002418425,0.0003336132,0.0000359866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005394813,"about_ca_system_score_gemma":0.00001794764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006694073,"about_ca_topic_score_gemma":0.00003628147,"domain_scores_codex":[0.9982308,0.0002319489,0.0003020764,0.0005558167,0.0002718743,0.0004074498],"domain_scores_gemma":[0.9991132,0.0001251968,0.0001031534,0.0004300143,0.00007459857,0.0001538383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001407131,0.0001465402,0.00001195115,0.000004088762,0.0001219723,0.00001459599,0.0001422528,0.851926,0.0004516279,0.000005676131,0.00008689029,0.1470743],"study_design_scores_gemma":[0.0002599147,0.00007070063,0.00007039015,0.00002878391,0.0002041873,0.0000207485,0.00006138888,0.9888713,0.009930297,0.0001017393,0.00008813536,0.0002924372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02966551,0.00006262276,0.9681693,0.00009420412,0.001241136,0.0001755972,0.00001428603,0.0002045681,0.0003727675],"genre_scores_gemma":[0.9879779,0.00009711301,0.01138892,0.0002795901,0.00004299706,0.00003171076,0.00000866086,0.00001708135,0.0001560927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9583123,"threshold_uncertainty_score":0.8669931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04051875161713008,"score_gpt":0.2650909615911927,"score_spread":0.2245722099740626,"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."}}