{"id":"W4317350113","doi":"10.1109/vtc2022-fall57202.2022.10012772","title":"Real-Time Emotion Recognition Using Deep Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Sadness; Disgust; Surprise; Computer science; Anger; Pride; Affective computing; Emotion classification; Artificial intelligence; Deep learning; Field (mathematics); Machine learning; Emotion recognition; Algorithm; Psychology; Social psychology","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","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001079189,0.0005437162,0.0006482117,0.001338339,0.001343135,0.00008209505,0.0006896328,0.000740765,0.01104114],"category_scores_gemma":[0.0001341457,0.0006565025,0.0002766567,0.001747218,0.0003259305,0.0002545191,0.0003576043,0.002335175,0.001592759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004730108,"about_ca_system_score_gemma":0.0001529331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004702408,"about_ca_topic_score_gemma":0.00007742766,"domain_scores_codex":[0.9952169,0.0009210361,0.0008185093,0.001340881,0.000684973,0.001017669],"domain_scores_gemma":[0.9979656,0.00009395635,0.000610582,0.0007713348,0.0003835977,0.0001749471],"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":[0.000382685,0.002080973,0.005073789,0.0001101089,0.001025135,0.001305905,0.003647102,0.001759812,0.2133049,0.007314251,0.002710165,0.7612852],"study_design_scores_gemma":[0.03057369,0.01361924,0.01579544,0.0009592163,0.003639245,0.01486833,0.1195696,0.527312,0.03670466,0.1171803,0.1054222,0.01435603],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966377,0.0002801005,0.01693934,0.00106542,0.002615022,0.0009871873,0.00008993223,0.001763649,0.009882317],"genre_scores_gemma":[0.9884452,0.0002843046,0.004353082,0.0003627371,0.0003112428,0.0007046651,0.001139906,0.0001666087,0.004232309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7469291,"threshold_uncertainty_score":0.9999665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03560155444448349,"score_gpt":0.2865867725549515,"score_spread":0.250985218110468,"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."}}