{"id":"W2976682141","doi":"10.3390/electronics8101088","title":"Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments","year":2019,"lang":"en","type":"article","venue":"Electronics","topic":"Face recognition and analysis","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China; Nvidia","keywords":"Artificial intelligence; Computer science; Facial recognition system; Convolutional neural network; Class (philosophy); Artificial neural network; Deep learning; Pattern recognition (psychology); Machine learning; Attendance; Face (sociological concept)","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":[0.0002570786,0.0001169661,0.0001129051,0.0001273003,0.0001246483,0.0001167302,0.0004172923,0.00004810945,0.0002223464],"category_scores_gemma":[0.00002101348,0.0001231489,0.00004659901,0.0003546625,0.000007995293,0.0005370123,0.00009161497,0.0002021173,0.0004433802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001384519,"about_ca_system_score_gemma":0.00008372989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003908234,"about_ca_topic_score_gemma":0.000008411827,"domain_scores_codex":[0.9986926,0.000116483,0.0001591916,0.0004345014,0.0003194817,0.0002777823],"domain_scores_gemma":[0.9993113,0.00004938782,0.0001077426,0.0004269819,0.00005180158,0.00005274753],"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":[0.00004946871,0.0002737965,0.001043834,0.00002257168,0.00009236246,0.000005366959,0.0002886817,0.05240274,0.0525701,0.0003020569,0.00007505958,0.8928739],"study_design_scores_gemma":[0.0004465441,0.0001242994,0.00005912076,0.00001832074,0.00001419984,0.000002799533,0.00003834666,0.9845157,0.01315059,0.0001745204,0.001295203,0.0001603515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1346321,0.0002419797,0.8641819,0.0001279855,0.0001183306,0.0001297549,0.000003127592,0.0001009331,0.0004638585],"genre_scores_gemma":[0.9722106,0.00006338457,0.02643921,0.0005568694,0.00003852541,0.000005952156,0.0005234847,0.00001566025,0.0001463145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.932113,"threshold_uncertainty_score":0.5698903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0435839519201968,"score_gpt":0.2987460188628792,"score_spread":0.2551620669426823,"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."}}