{"id":"W1490682138","doi":"10.1007/978-3-642-02611-9_51","title":"A Novel Technique for Human Face Recognition Using Nonlinear Curvelet Feature Subspace","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Curvelet; Artificial intelligence; Pattern recognition (psychology); Kernel principal component analysis; Computer science; Principal component analysis; Facial recognition system; Kernel (algebra); Wavelet; Wavelet transform; Feature extraction; Subspace topology; Face (sociological concept); Kernel method; Support vector machine; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008408201,0.0006237584,0.0005647371,0.0008885505,0.000549181,0.0005409101,0.002001202,0.0007061816,0.00000794642],"category_scores_gemma":[0.00008853609,0.0005928678,0.0002161601,0.0006047536,0.0002786354,0.0008288944,0.0005190306,0.0009491962,0.00001559253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003255816,"about_ca_system_score_gemma":0.0004220364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000387957,"about_ca_topic_score_gemma":0.00009585053,"domain_scores_codex":[0.9963896,0.00003001785,0.0004566002,0.001658921,0.0007452161,0.000719591],"domain_scores_gemma":[0.9976856,0.0002490289,0.0004158515,0.0009870784,0.000485158,0.0001772782],"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":[0.00002173392,0.000132331,0.000005101843,0.0001285364,0.00001607857,0.0000361628,0.0005914627,0.006302276,0.09842905,0.0009281537,0.0001476856,0.8932614],"study_design_scores_gemma":[0.001289562,0.0007860055,0.00003243536,0.003361517,0.00004088385,0.0004034236,0.00000106559,0.6627899,0.1452408,0.1788964,0.004749096,0.002408862],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001483818,0.000126711,0.9959676,0.0009677426,0.0006749631,0.001362715,0.00005729643,0.0002166775,0.0004778674],"genre_scores_gemma":[0.004254123,0.00001866735,0.993344,0.001360897,0.0005468964,0.00004156898,0.00007840343,0.00004629992,0.0003091053],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8908526,"threshold_uncertainty_score":0.9996523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04356246535080898,"score_gpt":0.2926561831340138,"score_spread":0.2490937177832048,"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."}}