{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004805468,0.0004737217,0.0008132778,0.000623312,0.0002345879,0.0003094029,0.0007052714,0.0003769687,0.003190825],"category_scores_gemma":[0.0005972952,0.0002038856,0.0004118944,0.0003369994,0.0001564825,0.0005586388,0.0003308194,0.0003468873,0.002009502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001999006,"about_ca_system_score_gemma":0.0002414866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008014873,"about_ca_topic_score_gemma":0.0006989611,"domain_scores_codex":[0.9995403,0.00006366091,0.00002361016,0.0001379711,0.0001971218,0.00003733371],"domain_scores_gemma":[0.9997695,0.00002823469,0.00002093467,0.0000273316,0.00013905,0.00001491126],"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":[0.0003019449,0.0001315098,0.00148689,0.0001316685,0.00004366429,0.0001036988,0.00007017795,0.002696063,0.4391117,0.001253524,0.00513048,0.5495387],"study_design_scores_gemma":[0.0001586752,0.001111504,0.03265569,0.00007492346,0.0002768475,0.002485129,0.0001663046,0.5408422,0.3809846,0.002120351,0.03893147,0.0001923023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05703083,0.0005505005,0.9309972,0.0001724203,0.0001864689,0.0002242352,0.000328095,0.00569268,0.004817613],"genre_scores_gemma":[0.3655197,0.0007214969,0.6167849,0.0002662492,0.0001476496,0.0005370376,0.001694247,0.0003161242,0.01401254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003190825,"threshold_uncertainty_score":0.01067436,"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."}}