{"id":"W4231886243","doi":"10.32920/14669016.v1","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; Facial expression; Face detection; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001819269,0.0001419554,0.0002369168,0.000142042,0.0002377572,0.0005832657,0.0002215958,0.0001363332,0.0001177776],"category_scores_gemma":[0.0000546741,0.000125305,0.0001753003,0.00009934708,0.0000152258,0.0001749059,0.0004462401,0.0001791752,0.000003337912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002553911,"about_ca_system_score_gemma":0.00003721766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005477629,"about_ca_topic_score_gemma":0.000225719,"domain_scores_codex":[0.9989983,0.00005749858,0.0002267794,0.0004377127,0.000133139,0.000146529],"domain_scores_gemma":[0.9993732,0.00006622319,0.0001024773,0.0002872759,0.00009483767,0.00007597594],"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":[0.00000129565,0.00008284275,0.00007063218,0.0003152512,0.0001201592,0.000006326453,0.001295047,0.00009463831,0.02147046,0.0006098994,0.0001205325,0.9758129],"study_design_scores_gemma":[0.0007191304,0.00005754122,0.003217625,0.0005664937,0.0001407894,0.00002057495,0.0006944695,0.9441081,0.0328394,0.01628896,0.0006191189,0.0007278006],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1494676,0.00004870364,0.8490465,0.0003028584,0.0002267969,0.0001930528,0.000005540596,0.0002173828,0.0004915954],"genre_scores_gemma":[0.9626358,0.00001857608,0.03683431,0.00010703,0.00006146138,0.00008361224,0.00002416787,0.000007337047,0.000227685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9750851,"threshold_uncertainty_score":0.5624445,"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."}}