{"id":"W2892320280","doi":"10.1109/ssci.2018.8628705","title":"Facial Recognition with Encoded Local Projections","year":2018,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Histogram; Pattern recognition (psychology); Support vector machine; Computer science; Histogram of oriented gradients; Feature (linguistics); Local binary patterns; Image (mathematics); Feature vector; Computer vision; Feature extraction","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006260606,0.00006700723,0.00005343694,0.00007213865,0.0001733362,0.00007597624,0.0001489355,0.00004121491,0.0002442683],"category_scores_gemma":[0.000007580475,0.00004697978,0.00001866523,0.0002764704,0.00007542593,0.0004962253,0.0000439436,0.00005750092,0.00123781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001407674,"about_ca_system_score_gemma":0.0000472614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006157062,"about_ca_topic_score_gemma":0.0001602594,"domain_scores_codex":[0.9993978,0.00002387911,0.00008392478,0.0002173965,0.000142248,0.0001348036],"domain_scores_gemma":[0.9996015,0.00001377333,0.00002653553,0.0001589509,0.000148431,0.00005078809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005121632,0.0001492691,0.0001571709,0.000006823053,0.00001611537,0.000006887579,0.0008127873,0.000006193338,0.004952012,0.002201411,0.01675283,0.9748873],"study_design_scores_gemma":[0.002797953,0.003266095,0.001815539,0.0002091784,0.00003141564,0.0002644061,0.001363899,0.123332,0.7775174,0.03080101,0.05736965,0.001231474],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03076353,0.000001334205,0.9187853,0.0004011186,0.0002051902,0.0001156279,0.0000017774,0.000263023,0.04946313],"genre_scores_gemma":[0.9230756,0.000001916232,0.07523238,0.000568927,0.0001509583,0.00003595086,0.000008414575,0.000004769934,0.0009211108],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9736558,"threshold_uncertainty_score":0.9995399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02439117010726922,"score_gpt":0.245455499166749,"score_spread":0.2210643290594798,"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."}}