{"id":"W4385781891","doi":"10.1007/s11042-023-16342-5","title":"CNN-Transformer based emotion classification from facial expressions and body gestures","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Transformer; Facial expression; Gesture; Deep learning; Benchmark (surveying); Pattern recognition (psychology); Emotion recognition; Gaussian; Emotion classification; Speech recognition; Machine learning","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":[0.0002103028,0.0007925048,0.0005072254,0.0004960548,0.0001477171,0.0004098958,0.0004045929,0.0003478149,0.005877005],"category_scores_gemma":[0.000392426,0.0001734704,0.0005903637,0.0004510732,0.0001304758,0.0003713656,0.0004448128,0.0004167683,0.002888284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003435533,"about_ca_system_score_gemma":0.000360528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005370818,"about_ca_topic_score_gemma":0.007997775,"domain_scores_codex":[0.9998341,0.00001247329,0.000006790854,0.00005032661,0.00004702064,0.00004926301],"domain_scores_gemma":[0.9999084,0.00001388123,0.000007586611,0.00001161328,0.00004801077,0.00001040636],"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":[0.0005503445,0.0001978972,0.004884623,0.0001545482,0.0001331511,0.0001469758,0.00003969398,0.007020628,0.1929469,0.0008375885,0.01141696,0.7816706],"study_design_scores_gemma":[0.00004404527,0.0004070293,0.04342656,0.00007513123,0.0002861213,0.0006969239,0.0001242042,0.8023831,0.1425521,0.001849371,0.008105875,0.00004955218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.328369,0.002458766,0.6227009,0.0005400069,0.001407569,0.0005361884,0.004627395,0.009348064,0.03001214],"genre_scores_gemma":[0.8772132,0.001589726,0.08824218,0.0003433851,0.000188531,0.0002425243,0.004602321,0.0002580491,0.02731995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005877005,"threshold_uncertainty_score":0.01966059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06660791186635369,"score_gpt":0.3343316742083807,"score_spread":0.267723762342027,"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."}}