{"id":"W75824681","doi":"10.1007/978-3-642-33564-8_70","title":"Facial Expression Recognition Using Game Theory and Particle Swarm Optimization","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Particle swarm optimization; Facial expression; Computer science; Novelty; Artificial intelligence; Pattern recognition (psychology); Scheme (mathematics); Facial expression recognition; Feature (linguistics); Expression (computer science); Wrinkle; Boundary (topology); Face (sociological concept); Computer vision; Texture (cosmology); Facial recognition system; Image (mathematics); Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008999094,0.0002800206,0.0002803097,0.0003813314,0.0002167872,0.0003898834,0.0005658973,0.0002024958,0.0000598526],"category_scores_gemma":[0.00008451549,0.0002532578,0.00007770303,0.0003691086,0.0002690026,0.0009498397,0.0004899532,0.0003065151,0.00002765057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001047385,"about_ca_system_score_gemma":0.0001070123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004761208,"about_ca_topic_score_gemma":0.000004342964,"domain_scores_codex":[0.9979802,0.00008566523,0.0003084378,0.0007605851,0.0004739743,0.0003910855],"domain_scores_gemma":[0.9988212,0.0002466057,0.0001998538,0.000408661,0.0001581979,0.0001654938],"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":[0.000006534206,0.00002096726,0.00002119484,0.00001516998,0.000006023849,0.000006546718,0.0005944767,0.09182449,0.0008145162,0.0008033579,9.817537e-7,0.9058858],"study_design_scores_gemma":[0.0001885043,0.00003484447,0.00001315827,0.0002446222,0.00001934302,0.00002450906,5.376444e-7,0.9456245,0.01019468,0.04319932,0.00007405718,0.0003819317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001247564,0.0004493297,0.997138,0.0001148532,0.000528239,0.0001624472,0.000004493204,0.00009176355,0.0002633698],"genre_scores_gemma":[0.3374722,0.0002278581,0.6608306,0.0008937849,0.0004448518,0.000006082497,0.00001462095,0.00002920411,0.00008074931],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9055038,"threshold_uncertainty_score":0.999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03032332299970226,"score_gpt":0.2552443857635676,"score_spread":0.2249210627638654,"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."}}