{"id":"W3014556410","doi":"10.1109/iceic49074.2020.9051332","title":"Facial Expression Recognition in Videos: An CNN-LSTM based Model for Video Classification","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on Electronics, Information, and Communication (ICEIC)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Benchmark (surveying); Convolutional neural network; Artificial intelligence; Facial expression; Convolution (computer science); Speech recognition; Recurrent neural network; Expression (computer science); Pattern recognition (psychology); Software; Deep learning; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004119089,0.0002003053,0.0001760893,0.0002317645,0.000281714,0.0004914707,0.0009018913,0.00011988,0.0000887518],"category_scores_gemma":[0.0001655919,0.0002184665,0.00006183385,0.0002250954,0.00004657335,0.003941153,0.00008396949,0.0003339752,0.00007803521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001624856,"about_ca_system_score_gemma":0.0003056646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001860398,"about_ca_topic_score_gemma":0.0001211682,"domain_scores_codex":[0.9982846,0.0001318295,0.0006688703,0.0003228659,0.0003584866,0.0002333358],"domain_scores_gemma":[0.9982746,0.0001026082,0.0004210123,0.0004070129,0.0006609699,0.0001337558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001114149,0.0006940591,0.0003934079,0.0001624203,0.00007620692,8.427199e-7,0.01368212,0.009975325,0.01148737,0.5421452,0.005387115,0.4148818],"study_design_scores_gemma":[0.0011282,0.0001954045,0.0003620901,0.00006264604,0.000006507883,0.000001593042,0.0003230121,0.9665349,0.002007122,0.02177681,0.007351071,0.0002506562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02171535,0.00007350714,0.9494842,0.02000817,0.0001638975,0.0009253384,0.0001185493,0.0002983497,0.00721264],"genre_scores_gemma":[0.9809954,0.0005214826,0.01089066,0.005553158,0.00006394876,0.0002792142,0.001655115,0.00001052754,0.00003054606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.95928,"threshold_uncertainty_score":0.8908803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08487108249038237,"score_gpt":0.294340779237867,"score_spread":0.2094696967474846,"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."}}