{"id":"W2077670765","doi":"10.1109/iscas.2012.6271426","title":"Human emotion recognition using a deformable 3D facial expression model","year":2012,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Facial expression; Isomap; Computer science; Artificial intelligence; Computer vision; Fiducial marker; Feature (linguistics); Discriminative model; Pattern recognition (psychology); Feature extraction; Affective computing; Feature vector; Nonlinear dimensionality reduction; Dimensionality reduction","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":[0.0002063536,0.0003508546,0.0002847923,0.0003803966,0.0001249708,0.0003988266,0.0003738674,0.0003984984,0.001343522],"category_scores_gemma":[0.0005861612,0.0001822281,0.0007745207,0.0002282941,0.0002545366,0.0003081291,0.0003325948,0.0003952152,0.0005413734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003093156,"about_ca_system_score_gemma":0.0002098227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002449897,"about_ca_topic_score_gemma":0.001799633,"domain_scores_codex":[0.9998249,0.00003331597,0.000007637014,0.00005401006,0.00006704163,0.00001309143],"domain_scores_gemma":[0.9999319,0.00001489995,0.000009397158,0.00001702643,0.00002095031,0.000005767582],"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":[0.0003553258,0.0001418805,0.003453709,0.00009113997,0.00009083721,0.0003496045,0.0003425794,0.2682476,0.3112811,0.008247933,0.003319793,0.4040785],"study_design_scores_gemma":[0.000006213142,0.00005324953,0.003435324,0.00000649992,0.00001076611,0.0001359987,0.00002270788,0.9840517,0.009767867,0.001243458,0.001248919,0.00001735219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0592144,0.000110935,0.9369769,0.0001379853,0.00005112968,0.00008370602,0.0001767528,0.0006990489,0.00254926],"genre_scores_gemma":[0.756539,0.0003419222,0.2375418,0.00009769142,0.00002997966,0.000197979,0.0004995684,0.0001015949,0.004650472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002449897,"threshold_uncertainty_score":0.004871249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1455827597880983,"score_gpt":0.3645846615179891,"score_spread":0.2190019017298908,"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."}}