{"id":"W2997716588","doi":"10.1142/s0219467819500220","title":"Face Identification Based on Discrete Wavelet Transform and Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Facial recognition system; Context (archaeology); Wavelet; Pattern recognition (psychology); Face (sociological concept); Authentication (law); Identification (biology); Relevance (law); Machine learning; Computer vision; Computer security","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":[0.0002683088,0.0002399677,0.0003065679,0.0004527186,0.0001560399,0.0003585764,0.0003060143,0.0003503209,0.001054664],"category_scores_gemma":[0.0007808305,0.0001563469,0.0002997038,0.0004662388,0.0002096739,0.0007280976,0.0002774222,0.0004772211,0.0003319577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002231405,"about_ca_system_score_gemma":0.0002561998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001220442,"about_ca_topic_score_gemma":0.001141084,"domain_scores_codex":[0.9998708,0.00002778649,0.00000570532,0.00002253816,0.00006103233,0.00001210051],"domain_scores_gemma":[0.9998759,0.00005746996,0.00001322015,0.00001284908,0.00003633518,0.000004148276],"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":[0.0001658832,0.00006237534,0.0009427579,0.0001276103,0.00006848631,0.0001588599,0.00008756476,0.2575546,0.04048003,0.02901107,0.001319379,0.6700213],"study_design_scores_gemma":[0.000002020862,0.00001616908,0.0003037212,0.000005628973,0.000006014413,0.00004325581,0.000006454363,0.991868,0.004047499,0.003015409,0.0006813338,0.000004485767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01904478,0.0003329157,0.9786152,0.00007685758,0.00004212151,0.00002048628,0.00002076435,0.0001934642,0.001653356],"genre_scores_gemma":[0.5243582,0.001062254,0.4686276,0.00005339989,0.00006483565,0.00008939907,0.0001106068,0.00004371676,0.005589982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001220442,"threshold_uncertainty_score":0.003528237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006847477560962715,"score_gpt":0.2479481766753026,"score_spread":0.2411006991143398,"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."}}