{"id":"W3094820928","doi":"10.18280/ts.370411","title":"A Facial Expression Recognition Model Based on Texture and Shape Features","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Support vector machine; Feature extraction; Classifier (UML); Computer vision; Facial expression; Facial recognition system; Three-dimensional face recognition; Convolutional neural network; Face detection","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009449466,0.0001638496,0.0001274802,0.00006553843,0.0001520536,0.0001408627,0.0002172238,0.00008767541,0.0002314627],"category_scores_gemma":[0.00001729264,0.0001346114,0.00005456957,0.000123656,0.00002127449,0.0003466484,0.00006779409,0.0001692046,0.00005245074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001209004,"about_ca_system_score_gemma":0.0000296351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001429643,"about_ca_topic_score_gemma":5.455871e-7,"domain_scores_codex":[0.9988235,0.00005798924,0.0001613361,0.0004168528,0.0003509851,0.0001893849],"domain_scores_gemma":[0.9995713,0.00004483143,0.00006272757,0.0001091531,0.00004374443,0.00016828],"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":[0.0004468258,0.0002719575,0.00007842168,0.00008243755,0.00001338194,0.00002776652,0.002370065,0.009259713,0.2150158,0.0002695601,0.03742964,0.7347345],"study_design_scores_gemma":[0.001010337,0.0002835848,0.0003624721,0.0001219205,0.000007385464,0.000001711032,0.00003454192,0.9660817,0.03040849,0.0008333185,0.0006486669,0.0002059147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1516032,0.00007802506,0.8346802,0.01006328,0.0001264171,0.0005746995,0.00008231696,0.0003945551,0.002397302],"genre_scores_gemma":[0.9775949,0.000009899606,0.01448548,0.007670201,0.0001257097,0.0000374915,0.00004840082,0.000009249807,0.00001870114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9568219,"threshold_uncertainty_score":0.5489295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03024614424429766,"score_gpt":0.2320205379838211,"score_spread":0.2017743937395235,"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."}}