{"id":"W4387421718","doi":"10.1145/3577190.3614141","title":"Toward Fair Facial Expression Recognition with Improved Distribution Alignment","year":2023,"lang":"en","type":"article","venue":"INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Computer science; Kernel (algebra); Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Facial expression; Attractiveness; Expression (computer science); Facial expression recognition; Machine learning; Mathematics; Facial recognition system; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003664398,0.001060956,0.001134826,0.0006780092,0.0004488997,0.001368086,0.001427423,0.001022632,0.001585909],"category_scores_gemma":[0.008620335,0.0003594999,0.0008040802,0.0004269219,0.001094381,0.001772584,0.002048792,0.002127446,0.001207705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000823753,"about_ca_system_score_gemma":0.0009807639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002052658,"about_ca_topic_score_gemma":0.001644505,"domain_scores_codex":[0.9980561,0.0006741417,0.00006723828,0.0004392669,0.0005697546,0.0001936146],"domain_scores_gemma":[0.9979358,0.0007246754,0.000244628,0.0005652973,0.0004177206,0.0001120429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007399453,0.000401671,0.006508508,0.00008818528,0.0001559334,0.0002160567,0.0004085109,0.4099703,0.05098284,0.02521846,0.006071386,0.4992382],"study_design_scores_gemma":[0.000009426975,0.00004593375,0.000526618,0.000006497251,0.0000110102,0.00006947129,0.0000233983,0.9835454,0.007190512,0.007910556,0.0006469576,0.00001421281],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03646761,0.0001952772,0.9607406,0.0002879857,0.00006203917,0.00004633545,0.00006212827,0.0007365019,0.001401658],"genre_scores_gemma":[0.8126819,0.0002925591,0.1796135,0.0005526304,0.0001419058,0.0001376065,0.0003476877,0.0002756693,0.005956552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003664398,"threshold_uncertainty_score":0.01937944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07736451981218141,"score_gpt":0.3196333882038205,"score_spread":0.2422688683916391,"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."}}