{"id":"W4242949851","doi":"10.4018/9781615209910.ch012","title":"From Face to Facial Expression","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Facial expression; Multidisciplinary approach; Expression (computer science); Face (sociological concept); Computer science; Facial recognition system; Everyday life; Human–computer interaction; Facial expression recognition; Artificial intelligence; Data science; Pattern recognition (psychology); Sociology; Epistemology; Social science","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":[0.0001915215,0.0009005783,0.0004693452,0.001266105,0.0005572495,0.002639552,0.00074007,0.001110071,0.03663238],"category_scores_gemma":[0.0007104689,0.0002399953,0.0003849213,0.00123311,0.001031723,0.003143546,0.001261585,0.001820097,0.01835258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007269599,"about_ca_system_score_gemma":0.0003220549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009344732,"about_ca_topic_score_gemma":0.001535811,"domain_scores_codex":[0.9998041,0.00002982522,0.000006722631,0.0000536935,0.00009039292,0.00001535839],"domain_scores_gemma":[0.9998826,0.00006146007,0.000004377969,0.00001695051,0.00002635881,0.000008196694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000242181,0.00002878668,0.0001689955,0.0007189359,0.00001358295,0.0001771315,0.001056225,0.0005449419,0.002979941,0.1099252,0.1864478,0.6979144],"study_design_scores_gemma":[0.000002881245,0.00001631011,0.0005405938,0.0005320109,0.000009185362,0.0008492633,0.0003329813,0.000704421,0.0009707997,0.04099416,0.9550334,0.00001409633],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.003830833,0.24575,0.06665548,0.008440885,0.006163583,0.00008731933,0.0007873116,0.001137691,0.6671469],"genre_scores_gemma":[0.04797428,0.214981,0.04425781,0.005844922,0.004285193,0.0001586788,0.001373633,0.0008827504,0.6802418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03663238,"threshold_uncertainty_score":0.1225476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848269739982406,"score_gpt":0.245437796861889,"score_spread":0.216955099462065,"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."}}