{"id":"W2910410849","doi":"10.1109/tip.2019.2893524","title":"Weighted Extreme Sparse Classifier and Local Derivative Pattern for 3D Face Recognition","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pattern recognition (psychology); Artificial intelligence; Extreme learning machine; Facial recognition system; Computer science; Classifier (UML); Sparse approximation; Autoencoder; ENCODE; Mathematics; Artificial neural network","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":[0.00015383,0.0001592502,0.0001476505,0.0001217138,0.0002735593,0.0002350172,0.0001630783,0.00007093496,0.00003844847],"category_scores_gemma":[0.000004421651,0.0001487348,0.00004633116,0.0002128879,0.00005674029,0.0007775319,0.000002779309,0.0002070936,0.0000734042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003391505,"about_ca_system_score_gemma":0.000048151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001748892,"about_ca_topic_score_gemma":0.000009358309,"domain_scores_codex":[0.9989488,0.00004974678,0.000178375,0.0004276421,0.000151914,0.0002435367],"domain_scores_gemma":[0.9994559,0.00009359208,0.00008483604,0.000181037,0.0001130821,0.00007155297],"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":[0.00002162068,0.00006556823,0.00001530833,0.00009078893,0.00001005752,0.000001598975,0.001022511,0.0002203257,0.001945712,0.000002776888,0.00002589661,0.9965779],"study_design_scores_gemma":[0.0008305752,0.0001501952,0.0001504448,0.0001600454,0.00001872059,0.00002004781,0.0002116938,0.9829583,0.01411307,0.0004019231,0.0007269388,0.0002580063],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01842532,0.0000419629,0.9797979,0.0006320003,0.0002198224,0.0002439367,0.000008329948,0.0001855373,0.0004452157],"genre_scores_gemma":[0.8987838,0.000007381125,0.1002784,0.0003088519,0.00002553889,0.0000356704,0.000003165504,0.0000182069,0.0005389858],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9963198,"threshold_uncertainty_score":0.606523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0314137331758309,"score_gpt":0.2595841419995221,"score_spread":0.2281704088236912,"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."}}