{"id":"W4312177448","doi":"10.18280/ria.360520","title":"Deep Neural Networks for Automatic Facial Expression Recognition","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Facial expression; Local binary patterns; Histogram; Classifier (UML); Deep learning; Pattern recognition (psychology); Facial recognition system; Facial expression recognition; Artificial neural network; Expression (computer science); Speech recognition; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003584495,0.0005947797,0.0003614317,0.0003945675,0.0001723579,0.0004497178,0.0006000243,0.0005779612,0.003203377],"category_scores_gemma":[0.0007830838,0.0002444821,0.0004415117,0.0004875096,0.0002589355,0.0005463635,0.00047808,0.001240364,0.001468803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005695655,"about_ca_system_score_gemma":0.0004341446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006086862,"about_ca_topic_score_gemma":0.007181341,"domain_scores_codex":[0.9997622,0.00004250557,0.00001361732,0.00006686338,0.00007750365,0.0000373197],"domain_scores_gemma":[0.9998808,0.0000343528,0.00001637757,0.0000209045,0.00004162998,0.00000595397],"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":[0.0001415172,0.0001064077,0.0008653803,0.0001706147,0.00008379818,0.00008525202,0.00004417783,0.1063038,0.03401291,0.01066389,0.02134474,0.8261776],"study_design_scores_gemma":[0.000006898529,0.00003648915,0.001088471,0.00003120436,0.00001834076,0.00005074633,0.00001409288,0.9729977,0.008692747,0.008845162,0.008204549,0.00001357008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03502577,0.01083642,0.935007,0.001452176,0.0004914242,0.00007699971,0.001009656,0.004344177,0.01175641],"genre_scores_gemma":[0.7341179,0.008053269,0.2179016,0.0008257651,0.0003052963,0.0002361716,0.003143224,0.0002739479,0.03514274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006086862,"threshold_uncertainty_score":0.01210284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04658577910050946,"score_gpt":0.2696359850984988,"score_spread":0.2230502059979893,"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."}}