{"id":"W2159088388","doi":"10.1109/crv.2013.17","title":"Online Facial Expression Recognition Based on Finite Beta-Liouville Mixture Models","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Facial expression; Computer science; BETA (programming language); Mixture model; Facial expression recognition; Expression (computer science); Artificial intelligence; Data modeling; Pattern recognition (psychology); Facial recognition system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141093,0.0005615823,0.0009396045,0.0005295029,0.0002553456,0.0007512766,0.00143752,0.0007639196,0.00119465],"category_scores_gemma":[0.003109929,0.0005178576,0.000933164,0.0003678933,0.000600357,0.001357774,0.000998371,0.001134813,0.0005915316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005346763,"about_ca_system_score_gemma":0.0004554672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002649436,"about_ca_topic_score_gemma":0.002520687,"domain_scores_codex":[0.9993492,0.0002452049,0.00002669986,0.0001588866,0.0001712745,0.00004873671],"domain_scores_gemma":[0.9993437,0.0004074773,0.00004854695,0.00006482401,0.0001076874,0.00002766352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003236033,0.0001312835,0.00274985,0.00008150985,0.000142144,0.0001404189,0.0002135319,0.5669376,0.05298265,0.01897507,0.001206295,0.3561162],"study_design_scores_gemma":[0.000001729963,0.000009458371,0.000125557,0.000001336783,0.000003195098,0.00001739424,0.00000333754,0.9968188,0.001345581,0.001541297,0.0001269625,0.000005372955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01073523,0.00009905836,0.9885681,0.00004093334,0.00001015089,0.00001035233,0.00001170472,0.0001910682,0.0003335163],"genre_scores_gemma":[0.6280207,0.0004469269,0.3672815,0.0001657378,0.00005271675,0.0001536232,0.0002971088,0.0001486997,0.003432961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002649436,"threshold_uncertainty_score":0.006034732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03488059973764252,"score_gpt":0.2404140554081176,"score_spread":0.2055334556704751,"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."}}