{"id":"W2195207531","doi":"10.1109/iccvw.2015.12","title":"Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?","year":2015,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":266,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nvidia","keywords":"Convolutional neural network; Computer science; Classifier (UML); Artificial intelligence; Facial expression; Facial recognition system; Pattern recognition (psychology); Facial expression recognition; Face (sociological concept); Deep learning; Expression (computer science); Action recognition; Speech recognition; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00162246,0.0006812065,0.0004227732,0.0003214788,0.0002141559,0.000996376,0.0008207302,0.000917098,0.002040457],"category_scores_gemma":[0.00868514,0.0003757331,0.0002902951,0.0003004834,0.000662706,0.002987799,0.0004504437,0.001338224,0.001095348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005833391,"about_ca_system_score_gemma":0.0004234754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003930899,"about_ca_topic_score_gemma":0.005724438,"domain_scores_codex":[0.9994822,0.0001685575,0.00001485507,0.000155638,0.00008430798,0.00009433396],"domain_scores_gemma":[0.9991419,0.00034886,0.0001111593,0.0001450841,0.0001952711,0.00005777782],"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":[0.0004961791,0.0002130473,0.05563827,0.0002616391,0.0002308286,0.0001261778,0.0004333963,0.08453024,0.03790805,0.01896365,0.01070033,0.7904983],"study_design_scores_gemma":[0.00003524267,0.0001540902,0.02663212,0.000130505,0.00006894339,0.0001048344,0.0003081972,0.9026764,0.02016648,0.04498393,0.004698512,0.00004075076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5198599,0.004334446,0.4389741,0.008913336,0.000667249,0.0001274869,0.001090042,0.001424439,0.0246092],"genre_scores_gemma":[0.9655115,0.0009385062,0.02855263,0.0004985032,0.00008605554,0.00004313679,0.0004518893,0.00009638663,0.00382142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003930899,"threshold_uncertainty_score":0.008580446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08126738009899337,"score_gpt":0.2811073713752072,"score_spread":0.1998399912762138,"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."}}