{"id":"W1550651188","doi":"10.1007/978-3-642-01513-7_36","title":"An Efficient Wavelet Based Feature Extraction Method for Face Recognition","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Facial recognition system; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Wavelet; Face (sociological concept); Computer vision","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":[0.0002456028,0.0005426952,0.0006531501,0.0007042966,0.0002400551,0.0003646615,0.0006763654,0.000488331,0.004612842],"category_scores_gemma":[0.0005009023,0.0003404753,0.0005779281,0.001003642,0.0001730525,0.0006544836,0.0004208806,0.0006474048,0.002797867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001760434,"about_ca_system_score_gemma":0.0003368386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009557774,"about_ca_topic_score_gemma":0.001499952,"domain_scores_codex":[0.9997994,0.00001752674,0.00001154692,0.00002559819,0.0001287652,0.00001721708],"domain_scores_gemma":[0.9998078,0.00005476958,0.00001382634,0.0000284875,0.00008598676,0.000009182887],"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":[0.0001187242,0.00006422051,0.0001275204,0.0001311424,0.00002950087,0.00006305241,0.00002413433,0.004271287,0.2097986,0.001971023,0.004915751,0.7784851],"study_design_scores_gemma":[0.00007137844,0.000405085,0.004824749,0.00006424933,0.0001850297,0.001565858,0.00005481832,0.644526,0.3015175,0.003713923,0.04298108,0.00009032715],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005245779,0.0004770509,0.9926419,0.00003712983,0.00008702511,0.00003548471,0.00008503496,0.0006299586,0.0007605591],"genre_scores_gemma":[0.0378241,0.001056063,0.9513295,0.00007311004,0.00008969451,0.0001266968,0.0005137558,0.0001700661,0.008816968],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004612842,"threshold_uncertainty_score":0.01543146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256749439189265,"score_gpt":0.3003327080841094,"score_spread":0.2746577641651829,"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."}}