{"id":"W2141721864","doi":"10.5539/mas.v3n5p31","title":"Research on Dynamic Facial Expressions Recognition","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Program for Changjiang Scholars and Innovative Research Team in University; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Facial expression; Computer science; Surprise; Hidden Markov model; Artificial intelligence; Pattern recognition (psychology); Mixture model; Vector quantization; Gaussian; Disgust; Facial expression recognition; Speech recognition; Expression (computer science); Facial recognition system; Psychology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001284568,0.0001399891,0.0001197213,0.0005592449,0.00112202,0.0004082891,0.001484833,0.0000822509,0.00003056],"category_scores_gemma":[0.00005418437,0.0001200847,0.00003668972,0.001507372,0.0002782957,0.0006999238,0.0002356142,0.0003970126,0.00103838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128927,"about_ca_system_score_gemma":0.0001688345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003284506,"about_ca_topic_score_gemma":0.000001619117,"domain_scores_codex":[0.996861,0.00005749257,0.0001925687,0.0008559633,0.001367327,0.000665612],"domain_scores_gemma":[0.9987627,0.00009267181,0.00005358816,0.0006745728,0.0001909023,0.0002255267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000133023,0.00008923399,5.849392e-7,0.000001196703,4.212337e-7,0.000002758653,0.0004167603,0.00008769079,0.5981664,0.003068237,0.0004147244,0.3977387],"study_design_scores_gemma":[0.0006857374,0.0003376592,0.001954658,0.0001309024,0.000002424586,0.000009430772,0.000259736,0.3591349,0.2075948,0.4281707,0.001170776,0.0005483517],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1765732,0.000019709,0.7198666,0.001732237,0.0003471968,0.0005186028,0.000007707557,0.0004357108,0.100499],"genre_scores_gemma":[0.9862033,0.00001137227,0.01276863,0.00075138,0.00003731926,0.00004615191,0.000005001747,0.000005200008,0.0001716328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8096301,"threshold_uncertainty_score":0.9997394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0766306965758321,"score_gpt":0.3589253986170575,"score_spread":0.2822947020412254,"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."}}