{"id":"W2054302069","doi":"10.1109/cw.2011.44","title":"Face Detection Using Skin Color Recursive Clustering and Recognition Using Multilinear PCA","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Principal component analysis; Computer vision; Feature (linguistics); Face (sociological concept); Feature extraction; Biometrics; Cluster analysis; Multilinear map; Feature vector; Face detection; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001501019,0.0001156322,0.0001057446,0.0001181377,0.0001927519,0.00008106283,0.0001162896,0.00009170592,0.00003976951],"category_scores_gemma":[0.00002950981,0.0001094137,0.00003219756,0.0001794304,0.0000289646,0.0008593732,0.0001453895,0.00009390659,0.00001915136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004031565,"about_ca_system_score_gemma":0.00001650252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004236792,"about_ca_topic_score_gemma":0.000068334,"domain_scores_codex":[0.9991412,0.00006248568,0.0001741025,0.0003182304,0.000111519,0.0001924785],"domain_scores_gemma":[0.9995574,0.0000308573,0.00008926229,0.0001500434,0.00009670301,0.00007571664],"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":[0.0001432119,0.0001542198,0.0002421271,0.00007585231,0.00003811893,0.00002438142,0.009432225,0.0007828464,0.2895084,0.00003273891,0.00002894898,0.6995369],"study_design_scores_gemma":[0.0002690695,0.00006292306,0.0002438856,0.00006562934,0.000009394582,0.00005090237,0.000442984,0.757091,0.2410843,0.0004987942,0.00002817492,0.0001528566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5013033,0.00001117575,0.4980639,0.000007092294,0.0001746533,0.0001096599,0.000001058515,0.00006677984,0.0002623395],"genre_scores_gemma":[0.7052138,0.00001327719,0.2946063,0.00009278346,0.00003455328,0.000004790443,0.00000154402,0.000007943207,0.00002499514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7563082,"threshold_uncertainty_score":0.4461762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09853609248491259,"score_gpt":0.2727829051522639,"score_spread":0.1742468126673514,"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."}}