{"id":"W2951151581","doi":"10.48550/arxiv.1510.02969","title":"Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nvidia","keywords":"Convolutional neural network; Computer science; Classifier (UML); Artificial intelligence; Facial expression; Pattern recognition (psychology); Facial recognition system; Face (sociological concept); Facial expression recognition; Action recognition; Expression (computer science); Speech recognition","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":[0.001437898,0.000679591,0.000402757,0.0003273586,0.0002067297,0.001041696,0.0007710349,0.0008664998,0.00224357],"category_scores_gemma":[0.008065077,0.0003732387,0.0002953787,0.0003078845,0.0006436104,0.002885878,0.0004496865,0.001391887,0.001239598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005543462,"about_ca_system_score_gemma":0.0003645258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003450603,"about_ca_topic_score_gemma":0.004963759,"domain_scores_codex":[0.9995317,0.00014953,0.00001324757,0.0001533266,0.00007222138,0.00008001424],"domain_scores_gemma":[0.999261,0.0002874729,0.00009739537,0.0001329058,0.0001691859,0.00005202878],"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":[0.0005245889,0.0002131339,0.05447065,0.0002778568,0.0002547553,0.0001124968,0.0004649007,0.0658702,0.03722276,0.02150823,0.01394271,0.8051376],"study_design_scores_gemma":[0.00004517045,0.0001707237,0.03636399,0.0001665718,0.00008906563,0.0001176508,0.0003620228,0.8660492,0.02105766,0.06888684,0.006640422,0.0000506937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5167843,0.005227308,0.4356827,0.01014017,0.0008000702,0.0001186252,0.001351834,0.001559993,0.02833498],"genre_scores_gemma":[0.9646732,0.001090893,0.02862372,0.0005620209,0.0001053103,0.00004267174,0.000549695,0.0001067646,0.004245673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003450603,"threshold_uncertainty_score":0.00760442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.203370727996214,"score_gpt":0.2602850645494182,"score_spread":0.05691433655320421,"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."}}