{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002525313,0.0004854604,0.0004420328,0.0008683847,0.0001128787,0.0005018821,0.000249441,0.000364803,0.001636379],"category_scores_gemma":[0.0009020035,0.0001667691,0.0005577418,0.0004970055,0.0001763314,0.0004616798,0.000387627,0.0002445241,0.0008835676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001941513,"about_ca_system_score_gemma":0.0001202073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00125626,"about_ca_topic_score_gemma":0.001156496,"domain_scores_codex":[0.9996972,0.00005854389,0.0000140922,0.00008133395,0.0001154879,0.00003344547],"domain_scores_gemma":[0.9998385,0.00003266277,0.00002551313,0.0000245142,0.0000685094,0.00001028386],"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":[0.0003604487,0.00009362595,0.007223919,0.0001349558,0.00008786442,0.0002956859,0.0002287934,0.01381011,0.2871174,0.001695778,0.004406625,0.6845447],"study_design_scores_gemma":[0.00003697743,0.0003194217,0.09582831,0.00006563684,0.0001222272,0.001565782,0.0003171496,0.7286328,0.1584918,0.004503042,0.009988773,0.0001281814],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2603869,0.0008321851,0.7275248,0.000259501,0.000159807,0.0001700308,0.001108731,0.002546507,0.007011572],"genre_scores_gemma":[0.8483852,0.0008939824,0.1453498,0.0001298916,0.00007514268,0.0001475021,0.001201571,0.0001235604,0.003693381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001636379,"threshold_uncertainty_score":0.005474269,"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."}}