{"id":"W4386637076","doi":"10.18280/isi.280402","title":"Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face recognition and analysis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Covenant University Centre for Research, Innovation and Discovery; Covenant University","keywords":"Invariant (physics); Facial recognition system; Computer science; Face (sociological concept); Computer security; Artificial intelligence; Pattern recognition (psychology); Mathematics; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003005943,0.0002683465,0.0003545158,0.000204704,0.0002187633,0.0003621329,0.0006710236,0.0005801628,0.001531721],"category_scores_gemma":[0.0004188797,0.0001707269,0.0003366181,0.0001190018,0.0002146271,0.0007530729,0.0004993677,0.0005038456,0.0008995159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003913361,"about_ca_system_score_gemma":0.0004466056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003181553,"about_ca_topic_score_gemma":0.003408219,"domain_scores_codex":[0.9998459,0.00001639031,0.000005190721,0.00006018371,0.00003407401,0.00003816108],"domain_scores_gemma":[0.9999067,0.00001707229,0.00001175349,0.00001606849,0.00004002546,0.000008344709],"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":[0.0002948531,0.0003080814,0.004315582,0.0000997066,0.00006599361,0.0003709204,0.0002622729,0.1639512,0.2426205,0.005303859,0.004160558,0.5782465],"study_design_scores_gemma":[0.000008963026,0.0001215976,0.001501836,0.000006082828,0.00001655317,0.0001104215,0.00002859666,0.966167,0.02904289,0.0007086433,0.002269908,0.00001755692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1380563,0.0002285807,0.8526543,0.0003025406,0.0001139998,0.0001415522,0.0000755592,0.002769837,0.005657294],"genre_scores_gemma":[0.8345872,0.0001836982,0.1582769,0.0002541132,0.00003823139,0.0001122023,0.0001158261,0.00003983861,0.006392035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003181553,"threshold_uncertainty_score":0.006326079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0277224719787958,"score_gpt":0.2342668623884107,"score_spread":0.2065443904096149,"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."}}