{"id":"W3109175299","doi":"10.18280/ria.340508","title":"Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Facial expression; Discriminative model; Artificial intelligence; Correlation; Pattern recognition (psychology); Optical flow; Computer science; Similarity (geometry); Face (sociological concept); Motion (physics); Class (philosophy); Support vector machine; Cognition; Affective computing; Image (mathematics); Speech recognition; Mathematics; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001336744,0.0001146947,0.0001502373,0.00008278499,0.0001047549,0.00001483424,0.00006833138,0.0001217168,0.0006667061],"category_scores_gemma":[0.0001985809,0.0001098401,0.00009088284,0.0001478119,0.00005934382,0.0000586328,0.00001265829,0.0001357569,0.0001538651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000110547,"about_ca_system_score_gemma":0.00001143103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000598371,"about_ca_topic_score_gemma":0.000006387178,"domain_scores_codex":[0.9990958,0.0000759613,0.0002854797,0.0003094981,0.00008277661,0.00015048],"domain_scores_gemma":[0.9993524,0.0002178444,0.0001239292,0.0001415181,0.00007925161,0.00008503194],"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":[0.001682292,0.0005189753,0.0003251914,0.0003374387,0.00005640447,0.000006146186,0.01287096,0.03504943,0.03555909,0.001810513,0.004094869,0.9076887],"study_design_scores_gemma":[0.0004018323,0.0009161655,0.001192934,0.0002827642,0.0000513882,0.000005287538,0.003064347,0.9542726,0.03476354,0.002538498,0.002220328,0.0002902761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1005162,0.000141861,0.8840297,0.003218294,0.0008637966,0.001032719,0.0003414838,0.0001067114,0.009749225],"genre_scores_gemma":[0.9961765,0.00001998117,0.002456978,0.0004055578,0.00007132012,0.00006928436,0.0002550209,0.00001753736,0.0005278414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9192232,"threshold_uncertainty_score":0.7299964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05904608732680376,"score_gpt":0.3223940667681708,"score_spread":0.263347979441367,"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."}}