{"id":"W4249322867","doi":"10.32920/14669016","title":"Automatic landmark point detection and tracking for human facial expressions","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Landmark; Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Particle filter; Face detection; Facial expression; Kernel (algebra); Object detection; Facial recognition system; Mathematics; Kalman filter","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000604765,0.0004629071,0.0006810153,0.001032336,0.0003440075,0.0004250519,0.0006328162,0.0005480541,0.001546194],"category_scores_gemma":[0.001930586,0.0003086816,0.0004090105,0.0008524448,0.0003322465,0.0006070434,0.0005634476,0.0004671047,0.001059942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003826109,"about_ca_system_score_gemma":0.0006823523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00341623,"about_ca_topic_score_gemma":0.002620991,"domain_scores_codex":[0.9993783,0.0001264464,0.00002054649,0.000172224,0.0002526304,0.00004974863],"domain_scores_gemma":[0.9996529,0.00009645331,0.00005236294,0.00007658217,0.0001060551,0.000015672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003969648,0.0001059993,0.003179371,0.0001190557,0.00005311167,0.0001635701,0.0001277451,0.07626667,0.170172,0.005411708,0.004956089,0.7390478],"study_design_scores_gemma":[0.00002102487,0.0000612552,0.003594734,0.000008021544,0.00001312828,0.0001779487,0.00003023554,0.9513715,0.03996193,0.002698788,0.002038539,0.00002298905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02899214,0.0001660851,0.9687612,0.00003363252,0.00002308275,0.00003911984,0.00006333271,0.001345813,0.0005755533],"genre_scores_gemma":[0.4717158,0.0003699686,0.5240261,0.00003668765,0.00003171076,0.0001444533,0.0005765817,0.0001997095,0.002898988],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00341623,"threshold_uncertainty_score":0.006792724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03588586702822693,"score_gpt":0.2937530559171767,"score_spread":0.2578671888889497,"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."}}