{"id":"W2113588087","doi":"","title":"Classification of upper and lower face action units and facial expressions using hybrid tracking system and probabilistic neural networks","year":2006,"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 Ottawa","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Probabilistic logic; Face (sociological concept); Feature (linguistics); Facial recognition system; Facial expression; Computer vision; Artificial neural network; Feature extraction; Facial motion capture; Tracking (education); Face detection; Feature vector; Probabilistic neural network; Time delay neural network","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":[0.0001016407,0.00009839897,0.0001175159,0.0000661628,0.0001747874,0.0001258477,0.0000646929,0.00005582202,0.00000237032],"category_scores_gemma":[0.00001647027,0.00008074797,0.00001210371,0.000132773,0.00005540859,0.0005708676,0.00009519974,0.00007764292,2.521053e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001525601,"about_ca_system_score_gemma":0.00001158163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007801965,"about_ca_topic_score_gemma":0.00000894501,"domain_scores_codex":[0.9992381,0.00006215162,0.0001958224,0.0002617887,0.0001118194,0.0001302616],"domain_scores_gemma":[0.9995731,0.00006503831,0.0001005895,0.0001236316,0.00008709858,0.00005050689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001550025,0.0002901902,0.02707735,0.0008453443,0.0000360547,0.00001934988,0.001389064,0.03432264,0.6216551,0.01453524,0.000989207,0.2986854],"study_design_scores_gemma":[0.0002232791,0.00002758941,0.02099774,0.0001284977,0.00001112792,0.00004683535,0.0002820351,0.9750754,0.002886547,0.0001734715,0.00003749382,0.0001099559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7846411,0.0000887549,0.2147464,0.00005209259,0.0001321068,0.0001525612,0.000003026596,0.00005374051,0.0001302783],"genre_scores_gemma":[0.998087,0.00001056057,0.001804866,0.00001292943,0.00003954069,0.000005805841,0.000005624483,0.00000507142,0.00002857005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9407528,"threshold_uncertainty_score":0.3292806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04955739329113534,"score_gpt":0.2624740382769427,"score_spread":0.2129166449858074,"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."}}