{"id":"W3153657223","doi":"10.1007/978-3-030-73103-8_51","title":"Performance Evaluation of Weighted Entropy Based Fusion Technique for Face Recognition with Different Pre-processing Techniques","year":2021,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Preprocessor; Histogram; Adaptive histogram equalization; Facial recognition system; Entropy (arrow of time); Histogram equalization; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001050229,0.0004930367,0.0006052612,0.0006504293,0.0002991425,0.0004403103,0.0006912184,0.000585493,0.002151409],"category_scores_gemma":[0.001370719,0.0001476263,0.0005463707,0.0005399215,0.000174814,0.0007636029,0.0004685335,0.0003315172,0.000473915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002648778,"about_ca_system_score_gemma":0.0002948419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001692883,"about_ca_topic_score_gemma":0.001390059,"domain_scores_codex":[0.9993739,0.0001202829,0.00003779941,0.00009034721,0.0003127062,0.00006504561],"domain_scores_gemma":[0.999239,0.0002831326,0.0000361932,0.00005832934,0.0003591612,0.00002421957],"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":[0.004599248,0.0005398584,0.004543583,0.0003723102,0.0003722976,0.000207008,0.0001539326,0.05018869,0.2342958,0.001505095,0.002690352,0.700532],"study_design_scores_gemma":[0.00003582433,0.001610196,0.009781204,0.00002138161,0.000251951,0.0004993564,0.00009509108,0.765054,0.2207611,0.000540435,0.001288115,0.00006137686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5788859,0.004787009,0.4076081,0.0001913447,0.0003849231,0.000109327,0.0003073833,0.001901002,0.00582502],"genre_scores_gemma":[0.8821945,0.001013033,0.1118502,0.00006262606,0.00006485425,0.00005357877,0.0006706994,0.00005983725,0.004030546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002151409,"threshold_uncertainty_score":0.007197142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03009856779073507,"score_gpt":0.2849787454754636,"score_spread":0.2548801776847286,"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."}}