{"id":"W2143484718","doi":"10.1109/icsmc.2009.5346252","title":"A robust wavelet based feature extraction method for face recognition","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Feature extraction; Artificial intelligence; Pattern recognition (psychology); Computer science; Facial recognition system; Wavelet; White noise; Robustness (evolution); Classifier (UML); Additive white Gaussian noise; Face (sociological concept); Feature (linguistics); Hidden Markov model; Wavelet transform; Gaussian; Speech recognition","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":[0.0005289365,0.0005886048,0.0008325998,0.0009680977,0.0002837886,0.000389594,0.0006469132,0.0007689939,0.002737957],"category_scores_gemma":[0.001205853,0.0003733704,0.0009066876,0.0009208241,0.0002961027,0.0007218716,0.0004289872,0.0008622881,0.002909586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002098215,"about_ca_system_score_gemma":0.0003471652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005352364,"about_ca_topic_score_gemma":0.0005444942,"domain_scores_codex":[0.9994312,0.00005967352,0.00003556716,0.000101402,0.0003352833,0.00003679788],"domain_scores_gemma":[0.9996706,0.00008403407,0.00004099745,0.00006432757,0.0001248708,0.00001508005],"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":[0.0001155642,0.00006710607,0.0002478214,0.0002480984,0.0000652021,0.0001213252,0.00003706603,0.006317201,0.2906079,0.002740906,0.00337299,0.6960589],"study_design_scores_gemma":[0.000069026,0.0005926118,0.005219831,0.00009728008,0.000201273,0.002869853,0.00005017952,0.4873401,0.4413728,0.003938786,0.05806652,0.0001817436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003262282,0.0003529586,0.9950024,0.00004857103,0.00008193857,0.0000450283,0.00007787832,0.0006081718,0.0005206513],"genre_scores_gemma":[0.04465974,0.0008203082,0.9490317,0.00009996982,0.00009974451,0.0001731505,0.0004380199,0.0001831476,0.004494114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002737957,"threshold_uncertainty_score":0.009159446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05471047128789513,"score_gpt":0.3083666133238127,"score_spread":0.2536561420359176,"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."}}