{"id":"W2008646744","doi":"10.5539/cis.v4n2p115","title":"Automatic Facial Expression Recognition System Based on Geometric and Appearance Features","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sadness; Computer science; Disgust; Surprise; Facial expression; Artificial intelligence; Pattern recognition (psychology); Anger; Expression (computer science); Feature (linguistics); Feature extraction; Emotion classification; Speech recognition; Psychology","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":[0.0004949759,0.0001224849,0.0001147183,0.0007205265,0.0004439924,0.0004529159,0.0003606263,0.00005220544,0.000007089038],"category_scores_gemma":[0.00003745163,0.00009648641,0.00002094154,0.0009189282,0.0001394904,0.006821335,0.0001707616,0.00009501206,0.00008250283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000256022,"about_ca_system_score_gemma":0.00004579457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005890774,"about_ca_topic_score_gemma":9.163142e-8,"domain_scores_codex":[0.9988287,0.00003406259,0.000243976,0.0002490586,0.0004350399,0.0002091295],"domain_scores_gemma":[0.9992962,0.00003739894,0.0001357683,0.000235831,0.0001573306,0.000137434],"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":[0.00001051086,0.00002137703,0.0001428876,0.0001051326,0.000001001519,0.000001080468,0.001687172,0.0000414372,0.0002096886,0.0008198939,0.0003383993,0.9966214],"study_design_scores_gemma":[0.0006383232,0.0002436485,0.05933572,0.0005199169,0.000003061532,0.00003843213,0.0001268026,0.9212148,0.01696196,0.0002220227,0.0004227721,0.0002725183],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2115557,0.00003799658,0.7813805,0.00007534039,0.0006271362,0.0002791467,0.000005156021,0.0003024647,0.005736586],"genre_scores_gemma":[0.9516191,0.00001296186,0.04763776,0.0006819926,0.00002518358,0.00001496224,0.000003827766,0.000001981123,0.000002156834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9963489,"threshold_uncertainty_score":0.4945303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02012680399924647,"score_gpt":0.215422235605221,"score_spread":0.1952954316059745,"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."}}