{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006419172,0.0004407985,0.0008371407,0.001011854,0.0002525729,0.0005285151,0.0008963781,0.0007799846,0.001188808],"category_scores_gemma":[0.00172709,0.000243324,0.0006605394,0.001091766,0.0003605376,0.000926004,0.000803989,0.0008848218,0.0006822415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000333078,"about_ca_system_score_gemma":0.0004383359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002027665,"about_ca_topic_score_gemma":0.002141405,"domain_scores_codex":[0.9993536,0.000119162,0.0000303434,0.0001040601,0.0003366891,0.00005614478],"domain_scores_gemma":[0.9995165,0.0001382397,0.00005375033,0.00007504581,0.0001909886,0.00002554038],"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":[0.0001866725,0.0001563617,0.002225818,0.00007564083,0.00009177863,0.0001481584,0.0000732644,0.1269183,0.04226029,0.008221922,0.004717613,0.8149241],"study_design_scores_gemma":[0.000003930594,0.00003708464,0.0004617553,0.000003222894,0.00000817313,0.00008751042,0.000007589711,0.994232,0.003161071,0.001352174,0.0006378659,0.00000755352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01328324,0.0001246272,0.9856439,0.00007071054,0.00003209807,0.00002190891,0.00004349674,0.0002967569,0.0004832628],"genre_scores_gemma":[0.490326,0.0004220565,0.5037923,0.0002415226,0.0001545536,0.0001853833,0.0007045061,0.00007228448,0.004101268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002027665,"threshold_uncertainty_score":0.004031777,"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."}}