{"id":"W3005980554","doi":"10.3390/s20041087","title":"eXnet: An Efficient Approach for Emotion Recognition in the Wild","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Innovation, Science and Economic Development Canada","keywords":"Overfitting; Computer science; Benchmark (surveying); Convolutional neural network; Artificial intelligence; Deep learning; Generalization; Facial expression; Feature (linguistics); Key (lock); Machine learning; Pattern recognition (psychology); Feature extraction; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002952191,0.00008501657,0.000086918,0.00005560947,0.00006096663,0.00001912235,0.00007740552,0.00008963233,0.0001465075],"category_scores_gemma":[0.00004917443,0.00006647212,0.00005501085,0.0001809118,0.00002532044,0.00002881682,0.000004725066,0.0001279254,0.0001663705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001167706,"about_ca_system_score_gemma":0.000005118299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001778293,"about_ca_topic_score_gemma":0.000006069085,"domain_scores_codex":[0.9990683,0.0002470941,0.0001617868,0.0002573707,0.00009820273,0.0001672564],"domain_scores_gemma":[0.9996892,0.00004971441,0.00005212293,0.000119942,0.00003738301,0.00005162618],"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":[0.00169034,0.004635423,0.001283021,0.0002819631,0.0001252719,0.00003540861,0.198103,0.01224309,0.001823369,0.007988463,0.02489347,0.7468972],"study_design_scores_gemma":[0.02030749,0.005625043,0.114027,0.0001470069,0.0003574384,0.0002745193,0.2380099,0.5685245,0.002078236,0.005252901,0.04310131,0.002294672],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650655,0.00001605352,0.008024491,0.001731825,0.0003176964,0.0007770158,0.00002360615,0.00007400082,0.02396976],"genre_scores_gemma":[0.9957823,0.000002645687,0.001131499,0.002217728,0.0003045248,0.0000690725,0.0003491437,0.00001548343,0.0001276261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7446025,"threshold_uncertainty_score":0.2710654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007163132348364,"score_gpt":0.3182011157437317,"score_spread":0.2174848025088953,"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."}}