{"id":"W14536956","doi":"","title":"A Robust Wavelet Based Feature Extraction Method.","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Feature extraction; Pattern recognition (psychology); Artificial intelligence; Computer science; Wavelet; White noise; Additive white Gaussian noise; Robustness (evolution); Classifier (UML); Wavelet transform; Facial recognition system; Feature (linguistics); Hidden Markov model; Gaussian","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":[0.0006848217,0.0007002705,0.0008253987,0.001327048,0.0003040269,0.0006373816,0.000701002,0.001045447,0.004163024],"category_scores_gemma":[0.00184653,0.0004294244,0.0009373351,0.001297654,0.0003787456,0.001129897,0.0006930131,0.001026989,0.005868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002125199,"about_ca_system_score_gemma":0.0003920637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000481389,"about_ca_topic_score_gemma":0.0004465324,"domain_scores_codex":[0.9990603,0.00009952885,0.00005676892,0.0001812658,0.0005442571,0.00005778351],"domain_scores_gemma":[0.9995209,0.0001121443,0.00006944731,0.0001084974,0.000166357,0.00002267614],"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":[0.000152593,0.00007825983,0.0003940178,0.0003795474,0.0001025801,0.0001670941,0.00005446207,0.007182279,0.243664,0.005200442,0.007648297,0.7349764],"study_design_scores_gemma":[0.0001041185,0.0005460043,0.006030409,0.0001705071,0.0002531848,0.003766726,0.00009240064,0.4736317,0.3861146,0.008787238,0.1202854,0.0002177307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00260972,0.000478672,0.9946937,0.00006604032,0.0001362351,0.00005793823,0.0001324442,0.000740669,0.001084498],"genre_scores_gemma":[0.06774989,0.001202401,0.9182553,0.0001992094,0.0001504346,0.0002486741,0.001013755,0.0003911376,0.01078928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004163024,"threshold_uncertainty_score":0.01392668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03246287009805182,"score_gpt":0.2920921174402545,"score_spread":0.2596292473422027,"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."}}