{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000294012,0.0001147229,0.0001083282,0.0001003228,0.0001291104,0.0001312415,0.0001932018,0.0001236235,0.00006649859],"category_scores_gemma":[0.00005783485,0.00009663012,0.00008755073,0.0002131616,0.000004428459,0.0006381544,0.00001247986,0.0001131519,0.00007389787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002573039,"about_ca_system_score_gemma":0.00003003554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000611304,"about_ca_topic_score_gemma":0.000004882763,"domain_scores_codex":[0.9991139,0.00006008382,0.000124743,0.0003438245,0.0001553033,0.0002021646],"domain_scores_gemma":[0.999362,0.0001437306,0.00006930136,0.0002253304,0.0001287177,0.0000709069],"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":[0.00002889769,0.00008187401,7.959587e-7,0.000008719324,0.000003312443,0.000001731909,0.00005097932,0.0001713678,0.02099273,0.0003914805,0.04756166,0.9307064],"study_design_scores_gemma":[0.0009803905,0.0002884057,0.0004967133,0.00005861463,0.00001354777,0.00001976369,0.00005985035,0.8059811,0.1614555,0.01143285,0.01890352,0.0003097106],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005076374,0.00001442142,0.9851056,0.01009714,0.000214978,0.0003192391,0.000008175063,0.0002681964,0.003464582],"genre_scores_gemma":[0.01756814,0.000004329333,0.9776781,0.003359458,0.00007814468,0.00004100307,0.00007062283,0.000005449711,0.001194744],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9303967,"threshold_uncertainty_score":0.3940462,"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."}}