{"id":"W2360392572","doi":"","title":"A Novel Method of Face Feature Extraction Based on 2DWT and Fisherfaces","year":2011,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Linear discriminant analysis; Computer science; Feature extraction; Face (sociological concept); Principal component analysis; Feature (linguistics); Discrete wavelet transform; Feature vector; Computer vision; Three-dimensional face recognition; Wavelet; Wavelet transform; Face detection","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000378143,0.0001121433,0.0001082245,0.00005955248,0.00005948297,0.00001101026,0.0001073249,0.00006154378,0.00001203292],"category_scores_gemma":[2.099823e-7,0.0001126186,0.00003019761,0.0001729373,0.0000245556,0.00006262563,0.0000158219,0.0001148312,0.000008775572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001523421,"about_ca_system_score_gemma":0.000005437299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009006129,"about_ca_topic_score_gemma":0.000002615798,"domain_scores_codex":[0.9995334,0.000004626539,0.0001233265,0.0001844881,0.00005014692,0.0001039706],"domain_scores_gemma":[0.9996346,0.00004783739,0.00003859886,0.0002028453,0.0000313457,0.00004477056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000598837,0.000276067,0.00006253871,0.00009509587,0.00003752115,2.19438e-7,0.0004854618,0.03737404,0.3570344,0.002966487,0.001507321,0.6001548],"study_design_scores_gemma":[0.0006521179,0.00005144072,0.009037535,0.00004025443,0.00004525539,0.00003100802,0.00009448018,0.3914075,0.2005832,0.001238505,0.3963711,0.0004475813],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006152688,0.00006977258,0.9970706,0.00008604271,0.000008979016,0.0003757034,0.00004605636,0.0001454277,0.001582124],"genre_scores_gemma":[0.03545929,0.00001483508,0.9640223,0.00006001963,0.00003905462,0.0002976818,0.00001671738,0.00002060606,0.00006945957],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5997072,"threshold_uncertainty_score":0.4592451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01481711729039357,"score_gpt":0.253447707346235,"score_spread":0.2386305900558414,"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."}}