{"id":"W2007371667","doi":"10.5539/cis.v2n4p169","title":"Two-Dimensional Heteroscedastic Discriminant Analysis for Facial Gender Classification","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Peking University; Zhejiang University; National Science Foundation","keywords":"Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Computer science; Discriminant; Classifier (UML); Covariance matrix; Projection (relational algebra); Gradient descent; Constraint (computer-aided design); Heteroscedasticity; Facial recognition system; Mathematics; Algorithm; Machine learning; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003624915,0.00009195427,0.0001108773,0.0004575126,0.0003999358,0.0005221369,0.0003665168,0.00002588658,0.000003059346],"category_scores_gemma":[0.00002361476,0.00007335587,0.00005591865,0.0008763559,0.0000856769,0.007005437,0.0000947369,0.00004447273,0.00001997265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002320108,"about_ca_system_score_gemma":0.0000571215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001577302,"about_ca_topic_score_gemma":2.226874e-7,"domain_scores_codex":[0.9989446,0.00001254425,0.000261881,0.0002289634,0.0003445834,0.0002073789],"domain_scores_gemma":[0.9992809,0.00003710109,0.0001083928,0.0002148649,0.0002484427,0.000110299],"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":[0.00003218472,0.00009900265,0.0009583484,0.00002794158,0.00003157229,6.365736e-7,0.003512639,0.03107578,0.00887705,0.08131272,0.001390372,0.8726817],"study_design_scores_gemma":[0.000263579,0.00007051666,0.1216274,0.000005506134,0.00001271967,0.000004128931,0.00002101492,0.8752974,0.0007359058,0.001448854,0.00040262,0.000110301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06159774,0.000006214791,0.9371656,0.0003611641,0.0002563939,0.0001441312,0.000006358287,0.00005890453,0.0004035608],"genre_scores_gemma":[0.9262686,0.000003164776,0.07209285,0.001562414,0.00003339259,0.00000875397,0.0000267376,7.644788e-7,0.000003316309],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8725715,"threshold_uncertainty_score":0.5078773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0448184794845715,"score_gpt":0.2955379758574044,"score_spread":0.2507194963728329,"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."}}