{"id":"W2031265081","doi":"10.1007/s11042-013-1548-z","title":"Face detection and facial expression recognition using simultaneous clustering and feature selection via an expectation propagation statistical learning framework","year":2013,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Yale University","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Feature selection; Cluster analysis; Feature (linguistics); Facial expression; Local binary patterns; Inference; Dirichlet process; Face (sociological concept); Facial recognition system; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.00008971168,0.000160235,0.0001283346,0.00009691907,0.0006270396,0.0004477694,0.00006491962,0.000186737,0.00001576389],"category_scores_gemma":[0.000126416,0.0001542537,0.00001301354,0.0001842996,0.00005485363,0.001329684,0.00006966718,0.0002725564,0.00001158538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003048031,"about_ca_system_score_gemma":0.00001157075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007210438,"about_ca_topic_score_gemma":0.00001803065,"domain_scores_codex":[0.9988794,0.0001015364,0.000194023,0.000478116,0.0001596039,0.0001873713],"domain_scores_gemma":[0.9991931,0.0002863442,0.000125314,0.0001099061,0.0001334895,0.0001518403],"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":[0.000009181207,0.00002713173,0.0001580554,0.00002393098,0.000002972557,3.071152e-7,0.0008986997,0.0006511388,0.2164863,0.00001124794,0.00000307139,0.781728],"study_design_scores_gemma":[0.0002588069,0.00009797716,0.003342629,0.00005634276,0.00001318289,0.00003288219,0.0004646842,0.977145,0.01671126,0.001520145,0.0001424367,0.0002146145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2777754,0.00003079164,0.7213187,0.00006768776,0.00003774795,0.0006278188,0.000007847576,0.000124976,0.000009017938],"genre_scores_gemma":[0.7846441,0.00005293525,0.2147359,0.00003081698,0.0001148631,0.0003203875,0.0000798505,0.00001173205,0.000009463151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9764939,"threshold_uncertainty_score":0.6290282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0201757162951749,"score_gpt":0.2651599190249089,"score_spread":0.244984202729734,"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."}}