{"id":"W2179228901","doi":"10.1016/j.jvcir.2015.09.007","title":"A novel approach for pain intensity detection based on facial feature deformations","year":2015,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Department of Science and Technology, Ministry of Science and Technology, India; McMaster University; University of Northern British Columbia","keywords":"Discriminative model; Facial expression; Artificial intelligence; Intensity (physics); Metric (unit); Feature vector; Pattern recognition (psychology); Feature (linguistics); Computer science; Support vector machine; Classifier (UML); Mathematics; Physics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001319845,0.00007838898,0.000129176,0.0001937796,0.0001762836,0.0001834833,0.0002311338,0.00006133901,8.058346e-7],"category_scores_gemma":[0.0006971941,0.00006566447,0.0000694816,0.0002054263,0.00003526446,0.001110372,0.0000544903,0.0001844337,0.000001348718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004952482,"about_ca_system_score_gemma":0.00005154798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001083127,"about_ca_topic_score_gemma":0.000002044463,"domain_scores_codex":[0.9990705,0.0002286337,0.0002669279,0.0001034296,0.0002491224,0.0000814278],"domain_scores_gemma":[0.9983087,0.0001956398,0.0003599923,0.0002308972,0.0008134813,0.00009135961],"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":[0.001905177,0.002391514,0.0006926568,0.0001406397,0.000096148,0.000001994744,0.007502993,0.008125808,0.1771276,0.0007505439,0.02269198,0.7785729],"study_design_scores_gemma":[0.0015773,0.0005208994,0.001004064,0.00004289094,0.00001411469,0.00003638429,0.001289032,0.9794627,0.01448146,0.0005567106,0.0009178331,0.00009656697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008161209,0.0000352301,0.9894487,0.001653442,0.00008713092,0.0001999847,0.000002444461,0.00002322672,0.0003886321],"genre_scores_gemma":[0.7596862,0.00002225918,0.2397615,0.0004085453,0.00004734119,0.00001499589,0.00003570345,0.000004673514,0.00001878393],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.971337,"threshold_uncertainty_score":0.2677719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06181218693585767,"score_gpt":0.3472417915356212,"score_spread":0.2854296045997635,"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."}}