{"id":"W2991818756","doi":"10.1142/s0218001420560066","title":"Local Comparative Decimal Pattern for Face Recognition","year":2019,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Face (sociological concept); Invariant (physics); Sample (material); Representation (politics); Computer vision; Set (abstract data type); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004320077,0.0001826646,0.0002617003,0.0002957514,0.00007163686,0.0002990955,0.0005405426,0.00009536337,0.0003149139],"category_scores_gemma":[0.00005922682,0.0001663692,0.0001631041,0.0001178245,0.00007016386,0.001021819,0.0000999019,0.0002282578,0.0005861074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005271563,"about_ca_system_score_gemma":0.00005173745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002116009,"about_ca_topic_score_gemma":0.00001890461,"domain_scores_codex":[0.9982176,0.00009068969,0.0007462186,0.0003113854,0.0004207822,0.0002133075],"domain_scores_gemma":[0.9977791,0.0003320572,0.00051053,0.0001173849,0.001131381,0.0001295877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001105006,0.000138968,0.0001828965,0.00001433794,0.00006304772,0.00001199788,0.0008791716,0.00009163622,0.002486614,0.00007393589,0.0001794146,0.9957675],"study_design_scores_gemma":[0.001697166,0.002360579,0.001679132,0.001941569,0.0000974174,0.0009365669,0.008533047,0.3270812,0.4802913,0.1692106,0.004716979,0.001454387],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2632007,0.00004284537,0.7337589,0.001027623,0.001536192,0.0001984986,0.00007129664,0.00002171897,0.0001421647],"genre_scores_gemma":[0.994311,0.0001026992,0.004191819,0.0009995156,0.0002823633,0.00001544258,0.00006352726,0.00001025971,0.00002336026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9943131,"threshold_uncertainty_score":0.753342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1269104456568531,"score_gpt":0.341547558349473,"score_spread":0.2146371126926199,"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."}}