{"id":"W4391041531","doi":"10.11591/ijeecs.v33.i2.pp960-970","title":"Acoustic and visual geometry descriptor for multi-modal emotion recognition fromvideos","year":2024,"lang":"en","type":"article","venue":"Indonesian Journal of Electrical Engineering and Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Support vector machine; Artificial intelligence; Face (sociological concept); Speech recognition; Modal; Landmark; Modalities; Similarity (geometry); Pattern recognition (psychology); Facial expression; Image (mathematics); Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002868779,0.000490093,0.000647199,0.001385645,0.0001748964,0.0005447119,0.0006185976,0.0004601078,0.002384211],"category_scores_gemma":[0.0007816129,0.000117326,0.0008102649,0.0007977168,0.0002278394,0.0006482995,0.0006352521,0.0004265233,0.00106435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003344239,"about_ca_system_score_gemma":0.0002974464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00253134,"about_ca_topic_score_gemma":0.002705116,"domain_scores_codex":[0.9995283,0.00006159383,0.00003382313,0.0001273884,0.0001913979,0.00005751308],"domain_scores_gemma":[0.9997724,0.00003653861,0.0000236538,0.000034478,0.0001133126,0.00001969551],"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":[0.0006000509,0.0001528426,0.001882375,0.0001733954,0.0000898695,0.0001508155,0.00007686733,0.01288896,0.1471585,0.002931342,0.01161529,0.8222797],"study_design_scores_gemma":[0.00004571296,0.0003410569,0.0198548,0.00003618591,0.0001097332,0.0007068413,0.0002648459,0.9049719,0.055629,0.003599454,0.0143596,0.00008096285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1152328,0.002480285,0.8711991,0.0003778682,0.0005543873,0.0002300684,0.001734023,0.002308659,0.005882696],"genre_scores_gemma":[0.7770628,0.001498262,0.2056627,0.000264113,0.0003829318,0.000315585,0.005628071,0.0001604088,0.009025069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00253134,"threshold_uncertainty_score":0.007975996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271239692460267,"score_gpt":0.2921430890858518,"score_spread":0.2650191198398251,"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."}}