{"id":"W2348555218","doi":"","title":"Robust face recognition based on low frequency DCT coefficients retransforming optimized by CLAHE","year":2014,"lang":"en","type":"article","venue":"Computer Engineering and Applications Journal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Biological Sciences","funders":"","keywords":"Adaptive histogram equalization; Discrete cosine transform; Pattern recognition (psychology); Artificial intelligence; Computer science; Histogram; Facial recognition system; Classifier (UML); Histogram equalization; Kernel (algebra); Contrast (vision); Computer vision; Mathematics; Image (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":[],"consensus_categories":[],"category_scores_codex":[0.000290492,0.0001530675,0.0001389512,0.0001552398,0.0002558867,0.0002855419,0.0003078462,0.00007445053,0.000008627901],"category_scores_gemma":[0.00001194275,0.0001415115,0.00005510876,0.000233403,0.00001553224,0.0002502582,0.0000294451,0.0002966431,0.00002476979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000306801,"about_ca_system_score_gemma":0.00001578789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001124818,"about_ca_topic_score_gemma":4.026825e-8,"domain_scores_codex":[0.999032,0.00003612045,0.0002410885,0.0002737177,0.0001967361,0.0002203633],"domain_scores_gemma":[0.9993365,0.0001101746,0.00007715814,0.0002251283,0.00008574792,0.0001652942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000605175,0.0001319013,0.00001075281,0.00003983653,0.00001225686,0.0000016513,0.0000895034,0.5945335,0.003031648,0.00026359,0.002241866,0.3996374],"study_design_scores_gemma":[0.0006400765,0.00007493173,0.00004100221,0.000166082,0.000005996357,0.00003570659,0.000003258399,0.9918134,0.001834616,0.0001907168,0.005006286,0.0001879643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004465118,0.00004921794,0.9944392,0.0003608147,0.0002022557,0.0001627403,0.00000648223,0.0001543339,0.0001598687],"genre_scores_gemma":[0.4347792,0.00008240911,0.5639588,0.0005920563,0.000395655,0.00008634666,0.00005280508,0.000026657,0.00002601973],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4304803,"threshold_uncertainty_score":0.5770672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087108710243183,"score_gpt":0.1975140228717637,"score_spread":0.1866429357693319,"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."}}