{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006635876,0.0009733884,0.001191731,0.002020086,0.000528878,0.0006785538,0.001100332,0.0008719213,0.004218407],"category_scores_gemma":[0.001299546,0.0005542133,0.001077084,0.001585286,0.0003931693,0.001871989,0.0008228958,0.001072942,0.002549461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003616787,"about_ca_system_score_gemma":0.0007720823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001770479,"about_ca_topic_score_gemma":0.001891713,"domain_scores_codex":[0.99921,0.00006833138,0.00003893137,0.0001593134,0.0004683615,0.00005499117],"domain_scores_gemma":[0.9996281,0.00007599722,0.00003385658,0.00005677026,0.0001890888,0.00001621836],"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":[0.00007708457,0.0000553989,0.0006597679,0.0001956681,0.00007020545,0.0001451455,0.00007448086,0.006382606,0.09366269,0.006084029,0.007432242,0.8851607],"study_design_scores_gemma":[0.00005507774,0.0002936313,0.005864914,0.00007398152,0.000126732,0.003010703,0.00009716251,0.7806351,0.1520322,0.009617265,0.0479812,0.0002119585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002295578,0.0001748857,0.9958407,0.00004656061,0.00008498223,0.00004814486,0.00006751612,0.0007253014,0.0007162458],"genre_scores_gemma":[0.03917678,0.0005566486,0.9547698,0.0001012684,0.0001068058,0.0002074119,0.0005130095,0.0001598607,0.004408417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004218407,"threshold_uncertainty_score":0.01411194,"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."}}