{"id":"W2112686571","doi":"10.1109/fgr.2006.71","title":"Learning to Identify Facial Expression During Detection Using Markov Decision Process","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; AdaBoost; Classifier (UML); Face detection; Random subspace method; Pattern recognition (psychology); Decision tree; Cascading classifiers; Machine learning; Hidden Markov model; Expression (computer science); Facial expression; Facial recognition system","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001644514,0.0001283512,0.0001078382,0.0002307801,0.000453551,0.0002769665,0.0002825129,0.0000844946,0.0000550055],"category_scores_gemma":[0.00005400512,0.0001133357,0.00004947211,0.0004292316,0.000007799522,0.0009745938,0.0002053355,0.0001400041,0.0001263679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005071028,"about_ca_system_score_gemma":0.00001646283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007680692,"about_ca_topic_score_gemma":0.00002139867,"domain_scores_codex":[0.9986472,0.00004897326,0.0002394989,0.0004176082,0.0003818036,0.0002649491],"domain_scores_gemma":[0.9995192,0.00003502934,0.00007519196,0.0001871949,0.0001021777,0.00008117181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003064172,0.00002628495,0.000866857,0.00001354305,0.000001040022,0.000006400316,0.0002034141,0.00888535,0.9246251,0.000004360954,0.00005820641,0.06527883],"study_design_scores_gemma":[0.0003334629,0.00003889289,0.01393312,0.0001632375,0.000002616562,0.00001970621,0.0001438407,0.04986854,0.9341444,0.0008973534,0.0002281842,0.0002265974],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5832539,0.000004956835,0.4158335,0.00001732021,0.0002063742,0.00009173089,1.820086e-7,0.0001709536,0.0004210528],"genre_scores_gemma":[0.9698601,0.00000138072,0.02965239,0.00003306005,0.0001210534,0.00001355937,0.000001526866,0.00001006388,0.000306882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3866062,"threshold_uncertainty_score":0.4621697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01320281938023242,"score_gpt":0.2898237635452554,"score_spread":0.2766209441650229,"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."}}