{"id":"W3152048897","doi":"10.18280/ria.350106","title":"Video Based Sub-Categorized Facial Emotion Detection Using LBP and Edge Computing","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sadness; Discriminative model; Happiness; Facial expression; Computer science; Feeling; Artificial intelligence; Face (sociological concept); Isolation (microbiology); Sight; Psychology; Affective computing; Cognitive psychology; Matching (statistics); Computer vision; Social psychology; Mathematics; Anger","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003168012,0.0001440665,0.0001712038,0.0001144137,0.0003642773,0.0002390345,0.0001853072,0.00009825329,0.00005208713],"category_scores_gemma":[0.0001289411,0.0001548457,0.00007798305,0.0005560038,0.00004498305,0.0003883507,0.0001427074,0.0001640048,0.0001140564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004764327,"about_ca_system_score_gemma":0.00006422034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002822154,"about_ca_topic_score_gemma":0.00001157764,"domain_scores_codex":[0.998597,0.0001268487,0.0003366653,0.0005046143,0.0001622159,0.0002727087],"domain_scores_gemma":[0.9991432,0.0001338439,0.00011056,0.0003283713,0.0001893116,0.00009468808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000138681,0.00009825028,0.0001574714,0.00006587225,0.000008018194,0.00002924017,0.0008634687,0.03178883,0.5501955,0.0005942326,0.0000310774,0.4161541],"study_design_scores_gemma":[0.00004406463,0.00002051298,0.00004481431,0.00005930273,0.000004542266,0.00002653719,0.0001629482,0.5463278,0.4522063,0.0007349556,0.000263437,0.0001048455],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3642654,0.0000922312,0.634665,0.0001797077,0.0004590452,0.00008456699,9.890059e-7,0.00008330815,0.0001696902],"genre_scores_gemma":[0.9865769,0.00002931016,0.01304742,0.0001570367,0.0001062076,0.000003665933,0.000007223115,0.000009945506,0.00006235758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6223114,"threshold_uncertainty_score":0.6314425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04730915288367296,"score_gpt":0.2710179695726276,"score_spread":0.2237088166889546,"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."}}