{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004602548,0.001232183,0.0006571906,0.0006910036,0.0002637106,0.0006269836,0.001756536,0.0007010762,0.005216042],"category_scores_gemma":[0.001301822,0.000348429,0.0006064264,0.0004129678,0.0003427813,0.001296626,0.001215082,0.0009701914,0.001963138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007234714,"about_ca_system_score_gemma":0.0005813024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006824453,"about_ca_topic_score_gemma":0.01244623,"domain_scores_codex":[0.9996818,0.00003709077,0.000008408034,0.0001237338,0.0001060745,0.00004278442],"domain_scores_gemma":[0.9998239,0.00003756619,0.00001422546,0.00005740404,0.00004966351,0.00001732496],"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":[0.0003739276,0.0002609208,0.003015662,0.0001535317,0.0001377728,0.000176581,0.00009988146,0.0919144,0.04359957,0.004383761,0.0417371,0.8141469],"study_design_scores_gemma":[0.00002174187,0.00008841675,0.001194447,0.00001127709,0.00001851327,0.0001588398,0.0000291109,0.9761006,0.0137833,0.004065104,0.00450931,0.00001943919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06758572,0.0008371326,0.8897802,0.0003328532,0.0002720014,0.0004199407,0.002933095,0.02931074,0.008528383],"genre_scores_gemma":[0.5154456,0.0006589607,0.4516954,0.0005852235,0.00009716913,0.0006182737,0.01312723,0.001098318,0.01667369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006824453,"threshold_uncertainty_score":0.01744938,"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."}}