{"id":"W3187067374","doi":"10.1371/journal.pone.0254965","title":"A face recognition software framework based on principal component analysis","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research, Innovation and Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Facial recognition system; Biometrics; Principal component analysis; Identification (biology); Software; Face (sociological concept); Process (computing); Artificial intelligence; Fingerprint (computing); Machine learning; Component (thermodynamics); Implementation; Principal (computer security); Iris recognition; Data mining; Pattern recognition (psychology); Software engineering; Computer security; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001065432,0.001354873,0.001058711,0.002012057,0.0006791079,0.001210783,0.002646071,0.0009971282,0.0121162],"category_scores_gemma":[0.002226023,0.0009418018,0.001810078,0.0008444392,0.0005727919,0.001389116,0.002114403,0.001954273,0.00792487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005330353,"about_ca_system_score_gemma":0.001636636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004942061,"about_ca_topic_score_gemma":0.003644415,"domain_scores_codex":[0.9991265,0.0000811361,0.00006351996,0.0001952195,0.0004419284,0.00009180765],"domain_scores_gemma":[0.9994549,0.0001350982,0.00003962784,0.00009892917,0.0002243512,0.00004700057],"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":[0.0004969833,0.0003569774,0.002508862,0.0009113832,0.0003689982,0.0008358332,0.0006426953,0.03344874,0.06755497,0.04624525,0.08082466,0.7658047],"study_design_scores_gemma":[0.0002652597,0.0003572933,0.004338724,0.0002837165,0.0002290226,0.002548285,0.0001352312,0.5600459,0.09171722,0.03925817,0.3003845,0.00043671],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001246952,0.0001442706,0.9388757,0.00005094627,0.00003858536,0.0001736617,0.000380024,0.05726602,0.001823744],"genre_scores_gemma":[0.04035369,0.0004916264,0.9411318,0.0002055887,0.00006061447,0.0009186025,0.003692353,0.006151882,0.006993884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0121162,"threshold_uncertainty_score":0.04053271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07484173792850403,"score_gpt":0.2473901462491626,"score_spread":0.1725484083206585,"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."}}