{"id":"W4362695238","doi":"10.31763/ijrcs.v3i2.939","title":"Improving the Recognition Percentage of the Identity Check System by Applying the SVM Method on the Face Image Using Special Faces","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Control Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Facial recognition system; Artificial intelligence; Support vector machine; Face (sociological concept); Pattern recognition (psychology); Computer science; Identity (music); Image (mathematics); Field (mathematics); Standard test image; Sample (material); Computer vision; Mathematics; Image processing","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":[0.001496112,0.0005937492,0.0009042627,0.0009860342,0.0002957537,0.000526576,0.0004797431,0.0005532812,0.001744582],"category_scores_gemma":[0.003042798,0.0001432614,0.0003701118,0.0003533117,0.0002011092,0.000869659,0.0004892391,0.0003907632,0.00124218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002283307,"about_ca_system_score_gemma":0.0003001126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001356677,"about_ca_topic_score_gemma":0.0007993124,"domain_scores_codex":[0.9988505,0.0002169858,0.00007942701,0.0002105848,0.0005191521,0.0001233975],"domain_scores_gemma":[0.9989612,0.0002862597,0.00007457645,0.0001619318,0.0004819644,0.00003415847],"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":[0.0007395648,0.0001812968,0.008704365,0.000188159,0.00008614639,0.0002068165,0.0001315228,0.008795302,0.1305466,0.0005041956,0.00334003,0.8465761],"study_design_scores_gemma":[0.00005991131,0.00129033,0.05976139,0.00006288419,0.0002229753,0.002062241,0.0002766903,0.595437,0.3321086,0.0005984441,0.0079891,0.0001304929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5376416,0.002169396,0.445538,0.0003283845,0.0004705354,0.0001334121,0.00020835,0.005865577,0.007644758],"genre_scores_gemma":[0.8695257,0.0004909243,0.1264009,0.00008307696,0.00005109308,0.00004317237,0.0003163832,0.0001034584,0.00298516],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001744582,"threshold_uncertainty_score":0.007912278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02647688488962778,"score_gpt":0.279577055202665,"score_spread":0.2531001703130372,"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."}}