{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002217254,0.00006834826,0.00008195297,0.0001708625,0.00003487668,0.0002234748,0.0002494502,0.00003608132,0.000008149512],"category_scores_gemma":[0.00001635038,0.00005296414,0.0000539878,0.000075269,0.0000297024,0.0007104421,0.0000243799,0.0001589834,0.000001925494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000669071,"about_ca_system_score_gemma":0.00001101637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002874299,"about_ca_topic_score_gemma":0.00000109644,"domain_scores_codex":[0.9992805,0.00002655233,0.000213488,0.0001115271,0.0002942735,0.00007372016],"domain_scores_gemma":[0.9994487,0.00007457616,0.0001459163,0.00007903883,0.0001980187,0.00005380765],"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":[0.0009511485,0.0003964279,0.01751948,0.00009775595,0.0003379422,0.0003225747,0.002216589,0.01047761,0.02888285,0.01941789,0.005029053,0.9143507],"study_design_scores_gemma":[0.0007966988,0.0001554861,0.01147513,0.00008486791,0.000007581923,0.00007945885,0.0000429655,0.9823588,0.00197529,0.002463319,0.000464028,0.00009644137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2360922,0.0001075942,0.7560709,0.006770284,0.0006712678,0.00007181781,0.000005943,0.00001011149,0.0001998566],"genre_scores_gemma":[0.9972045,0.0002244253,0.001642324,0.000829226,0.00005892936,7.918906e-7,0.000004813428,0.000003420586,0.00003154784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9718811,"threshold_uncertainty_score":0.2159815,"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."}}