{"id":"W4312102722","doi":"10.1109/tencon55691.2022.9977693","title":"Combating Uncertainty and Class Imbalance in Facial Expression Recognition","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Computer science; Class (philosophy); Facial expression; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Block (permutation group theory); Machine learning; Expression (computer science); Facial recognition system; Intersection (aeronautics); Noise (video); Fuzzy logic; Data mining; Mathematics; Image (mathematics); Engineering","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009281017,0.0003983082,0.0005234361,0.0004841169,0.0005828721,0.00007877243,0.0003770487,0.0002414964,0.01453532],"category_scores_gemma":[0.0001291104,0.0004258793,0.0001359476,0.0006588611,0.0002292726,0.0003257625,0.0002152543,0.00113006,0.0002618759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002522774,"about_ca_system_score_gemma":0.0001665398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000397285,"about_ca_topic_score_gemma":0.0002916385,"domain_scores_codex":[0.9958068,0.001169829,0.0007521903,0.001056005,0.0005539036,0.0006612536],"domain_scores_gemma":[0.9983746,0.0002703606,0.0005016392,0.0004592454,0.0001861413,0.0002079598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003887616,0.003020762,0.03894021,0.0004088873,0.0002742308,0.001381815,0.02867,0.0005072822,0.1018419,0.008027741,0.1336385,0.6794011],"study_design_scores_gemma":[0.05816599,0.01175544,0.1087233,0.002879704,0.0006413365,0.004276592,0.3101119,0.06642356,0.01413145,0.09731735,0.3128246,0.01274877],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9560288,0.0003506551,0.0008505643,0.002318301,0.003418026,0.00117843,0.000195526,0.0003194718,0.03534018],"genre_scores_gemma":[0.9910632,0.0002496596,0.0001037744,0.0009547148,0.0002062023,0.0007356948,0.0004234223,0.0000469615,0.006216351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6666523,"threshold_uncertainty_score":0.9998193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06700933578632123,"score_gpt":0.30304759493473,"score_spread":0.2360382591484088,"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."}}