{"id":"W4285464816","doi":"10.32920/ryerson.14646201","title":"Human emotional state recognition using 3D facial expression features","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Isomap; Discriminative model; Feature extraction; Pattern recognition (psychology); Robustness (evolution); Computer vision; Facial expression; Support vector machine; Gesture recognition; Gesture; Dimensionality reduction; Nonlinear 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002231113,0.0003545025,0.0003286048,0.0002428534,0.0003822179,0.0008694436,0.0006408424,0.0003987437,0.000677952],"category_scores_gemma":[0.00003329832,0.000330354,0.0002098533,0.0001711094,0.00003870347,0.000707528,0.001864453,0.0007461869,0.0000702482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008978966,"about_ca_system_score_gemma":0.0002061242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001651061,"about_ca_topic_score_gemma":0.00003075528,"domain_scores_codex":[0.9974051,0.0002243137,0.0004221137,0.0009880753,0.0006119434,0.0003484196],"domain_scores_gemma":[0.9985835,0.00003229633,0.0002753313,0.0006206987,0.000344216,0.0001439912],"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":[0.00003677106,0.0007882531,0.000209377,0.0006336896,0.000158261,0.0002497188,0.003613763,0.009842957,0.4277214,0.0001453351,0.02668462,0.5299159],"study_design_scores_gemma":[0.00279494,0.0002147201,0.0116272,0.009873156,0.0001250709,0.0002513682,0.0007210909,0.109899,0.7764521,0.08101382,0.002107914,0.004919684],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5150067,0.0001393753,0.4775263,0.0002463258,0.002256151,0.0003851306,0.00007615155,0.0004639155,0.003899943],"genre_scores_gemma":[0.3513353,0.0001057708,0.6430549,0.0009653858,0.0007723848,0.00008649955,0.001809832,0.00005944486,0.001810438],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5249962,"threshold_uncertainty_score":0.9999148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05594265068750391,"score_gpt":0.2989621474173431,"score_spread":0.2430194967298391,"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."}}