{"id":"W4382394488","doi":"10.18280/ts.400319","title":"Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Facial expression recognition; Deep learning; Artificial intelligence; Computer science; Expression (computer science); Facial expression; Pattern recognition (psychology); Speech recognition; Facial recognition system; Programming language","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":[0.0002683905,0.0005312326,0.0003440455,0.0002691685,0.0002128835,0.0003479513,0.0009299917,0.0005784053,0.001500062],"category_scores_gemma":[0.0004979415,0.0001806257,0.000493256,0.0002110891,0.0002262227,0.0003702331,0.0003466891,0.0006443313,0.0005093698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006660634,"about_ca_system_score_gemma":0.0005439755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01579971,"about_ca_topic_score_gemma":0.01577891,"domain_scores_codex":[0.9998877,0.00001097997,0.000004243416,0.00003389675,0.00003521976,0.00002796582],"domain_scores_gemma":[0.9999077,0.0000205724,0.000008640774,0.000008178456,0.00004892056,0.000006064106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001896758,0.0001378107,0.002497417,0.00006582333,0.00008410008,0.0001713403,0.00007002034,0.5911355,0.03090649,0.003037339,0.003669519,0.368035],"study_design_scores_gemma":[0.000001164529,0.00001596775,0.0002772549,0.000003398116,0.000005370253,0.00001366573,0.000003976496,0.9977125,0.001469437,0.0002326086,0.0002619855,0.000002710607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1266116,0.0008726427,0.8611649,0.000497259,0.0001834821,0.0000836645,0.0002347631,0.001420081,0.008931635],"genre_scores_gemma":[0.884047,0.000521053,0.1014098,0.0002630572,0.00004540795,0.0001176139,0.0005154985,0.00004913672,0.01303142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01579971,"threshold_uncertainty_score":0.03141546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07496411765075692,"score_gpt":0.2735325855066259,"score_spread":0.198568467855869,"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."}}