{"id":"W2003791493","doi":"10.1155/2011/745487","title":"Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications","year":2011,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Local binary patterns; Artificial intelligence; Discriminative model; Computer science; Pattern recognition (psychology); Histogram; Biometrics; Feature extraction; Pixel; Face (sociological concept); Computer vision; Face detection; Facial recognition system; Feature (linguistics); Overhead (engineering); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000346932,0.0002775728,0.0003719321,0.001185024,0.0001775624,0.000333316,0.0003481449,0.0002940925,0.001773722],"category_scores_gemma":[0.001403574,0.0001515646,0.0002192611,0.0006704933,0.0001345776,0.0003893904,0.0002357552,0.0002807212,0.0006331701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002101719,"about_ca_system_score_gemma":0.0002410401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001089254,"about_ca_topic_score_gemma":0.001690955,"domain_scores_codex":[0.9997746,0.0000469261,0.00001278745,0.00003858288,0.0001039557,0.00002315826],"domain_scores_gemma":[0.9993353,0.0002099725,0.00008244472,0.00006556563,0.0002603191,0.00004632437],"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":[0.001025898,0.0003577272,0.008396653,0.0001435329,0.00005474269,0.0002429119,0.00005440844,0.01634979,0.2158164,0.0007965926,0.004334805,0.7524266],"study_design_scores_gemma":[0.00003253666,0.0002874527,0.01570132,0.00001677564,0.00004731984,0.0004303406,0.000046817,0.8789651,0.1021468,0.0005633509,0.001740313,0.00002188705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4512574,0.0009718292,0.5420716,0.0002609761,0.0001139538,0.0001091625,0.0003117503,0.001974335,0.002929078],"genre_scores_gemma":[0.8620991,0.0002426266,0.1362368,0.0000448701,0.0000439826,0.00004596589,0.0002641313,0.00004338528,0.0009792165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001773722,"threshold_uncertainty_score":0.005933642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02365659627306402,"score_gpt":0.2859235098835882,"score_spread":0.2622669136105242,"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."}}