{"id":"W1968985103","doi":"10.1142/s0218001411008762","title":"CLASSIFYING FACIAL EXPRESSIONS USING LEVEL SET METHOD BASED LIP CONTOUR DETECTION AND MULTI-CLASS SUPPORT VECTOR MACHINES","year":2011,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Concordia University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Pattern recognition (psychology); Support vector machine; Facial expression; Computer science; Computer vision; Classifier (UML); Feature vector; Feature (linguistics); Face (sociological concept); Facial muscles","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":[0.0005124821,0.0001753489,0.0001950075,0.0003612601,0.0001581098,0.0002280937,0.0003313209,0.0001050881,0.0001991528],"category_scores_gemma":[0.0001610274,0.0001558148,0.0001000117,0.0001059076,0.00006702205,0.0007066538,0.0001140886,0.0002569383,0.00002145719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003619393,"about_ca_system_score_gemma":0.00006425387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001240399,"about_ca_topic_score_gemma":0.00009006323,"domain_scores_codex":[0.9984009,0.0001832542,0.000626005,0.0002789384,0.0003339896,0.0001769683],"domain_scores_gemma":[0.9985528,0.0001378893,0.00046919,0.00009461836,0.0005856666,0.0001598458],"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":[0.0001057771,0.0001392128,0.0006434909,0.00001137131,0.00004423163,0.00004592096,0.001273205,0.00005386973,0.07571358,0.00002612295,0.00001377131,0.9219294],"study_design_scores_gemma":[0.0004883439,0.0003207783,0.003175234,0.0004585853,0.00004840648,0.000545481,0.0008933004,0.6139038,0.3735031,0.006015715,0.0002216113,0.0004255953],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2088852,0.00001918838,0.7894461,0.0002188214,0.001240055,0.00008034457,0.00005558438,0.00002109717,0.00003364144],"genre_scores_gemma":[0.9281818,0.00004492256,0.07098039,0.0004802232,0.0002768012,0.000005498064,0.000009178292,0.00001118775,0.00001003774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9215038,"threshold_uncertainty_score":0.6353943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3578585071330138,"score_gpt":0.3821586223753324,"score_spread":0.02430011524231857,"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."}}