{"id":"W2132450737","doi":"10.1109/iccsa.2011.69","title":"PCA Based Geometric Modeling for Automatic Face Detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Face detection; Face (sociological concept); Pattern recognition (psychology); Feature extraction; Geometric transformation; Edge detection; Object-class detection; Principal component analysis; Facial recognition system; Pixel; Feature (linguistics); Image (mathematics); Image processing","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":[0.0003797862,0.000846767,0.0006971263,0.001180002,0.0003678323,0.0006016905,0.0009053852,0.0005929752,0.003678968],"category_scores_gemma":[0.001026385,0.0004684508,0.001056733,0.001069834,0.0004022188,0.0008446017,0.0004851435,0.0008414501,0.002807331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004077311,"about_ca_system_score_gemma":0.0005796333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00297005,"about_ca_topic_score_gemma":0.002044999,"domain_scores_codex":[0.9992732,0.0001144052,0.00001937282,0.0001407052,0.0004134492,0.00003872092],"domain_scores_gemma":[0.9997045,0.00008126826,0.00003221072,0.00007040241,0.0001029443,0.000008566118],"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":[0.00008979264,0.00005199184,0.0008099169,0.0002106391,0.0000910829,0.0001873415,0.00008830323,0.1875025,0.06679377,0.03290789,0.008813282,0.7024536],"study_design_scores_gemma":[0.000004043843,0.00004152902,0.0008379389,0.00001246711,0.00001582421,0.0002596569,0.00001245292,0.9670459,0.01398363,0.005487014,0.01227032,0.00002911078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001735343,0.0002490482,0.9958647,0.0000479457,0.00004019303,0.00002061726,0.00004791525,0.0008882996,0.001105832],"genre_scores_gemma":[0.1660032,0.001691162,0.8243188,0.0001182348,0.000141411,0.0001985872,0.0008342928,0.0003891567,0.00630522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003678968,"threshold_uncertainty_score":0.01230741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06905308257624158,"score_gpt":0.2470178802095648,"score_spread":0.1779647976333232,"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."}}