{"id":"W4296211812","doi":"10.1109/tai.2022.3207450","title":"FaceTopoNet: Facial Expression Recognition Using Face Topology Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Face recognition and analysis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Facial expression recognition; Computer science; Facial recognition system; Facial expression; Topology (electrical circuits); Pattern recognition (psychology); Artificial intelligence; Face (sociological concept); Speech recognition; Mathematics; Combinatorics; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003270347,0.0001894757,0.0002026486,0.0004019388,0.001779744,0.000169705,0.0004701441,0.00007540658,0.001882372],"category_scores_gemma":[0.00001503663,0.0002134682,0.000191329,0.0009121653,0.00009751914,0.0003526484,0.00001466965,0.0006667853,0.0003959953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000149108,"about_ca_system_score_gemma":0.00008206787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001056543,"about_ca_topic_score_gemma":0.00003673463,"domain_scores_codex":[0.9978796,0.0003943662,0.0004062472,0.0005482847,0.00042037,0.0003510918],"domain_scores_gemma":[0.999271,0.0001231409,0.0001346884,0.0002779014,0.0000815113,0.0001118008],"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":[0.00003362351,0.00021621,0.00000150174,0.000003494904,0.00001715496,0.00001083519,0.001280613,0.4010325,0.04485486,0.0001793143,0.00001067284,0.5523592],"study_design_scores_gemma":[0.00003900385,0.0001869243,0.000001085022,0.00001194564,0.00001909648,0.00002895711,0.00196358,0.5452651,0.4482957,0.003165718,0.0007488523,0.0002739277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05687219,0.00001703388,0.9410154,0.00041873,0.001058112,0.0001617504,0.00002362414,0.0002449376,0.0001881948],"genre_scores_gemma":[0.991318,0.00002167995,0.008033591,0.0002512697,0.00004332793,0.00005827814,0.000006597922,0.00001496116,0.000252326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9344458,"threshold_uncertainty_score":0.9995198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09058653516303239,"score_gpt":0.3079096537704102,"score_spread":0.2173231186073778,"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."}}