{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001271748,0.0001242951,0.0002343238,0.0001784447,0.0001232395,0.0001327737,0.0002566059,0.0001035435,0.0003738515],"category_scores_gemma":[0.0002696105,0.0001235409,0.0001302275,0.0009885718,0.00001457396,0.0001743681,0.0001050835,0.0002211206,0.0005392616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003969035,"about_ca_system_score_gemma":0.00005192268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005793323,"about_ca_topic_score_gemma":0.00000557766,"domain_scores_codex":[0.9984648,0.0001276151,0.0001904508,0.0004464318,0.0005566248,0.0002140812],"domain_scores_gemma":[0.9987962,0.000252739,0.00007942373,0.0005596413,0.00019817,0.0001138348],"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":[0.0008191418,0.08747144,0.07621808,0.001949346,0.0131478,0.0018119,0.008279963,0.04212821,0.1323335,0.003878413,0.005918351,0.6260439],"study_design_scores_gemma":[0.0007835033,0.0002295041,0.01179177,0.001209068,0.0007101435,0.000001910941,0.00007367018,0.7032033,0.2756976,0.005388082,0.0002588787,0.0006526599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3060065,0.00002847917,0.6915858,0.001514071,0.00006323925,0.0001214391,0.00002502449,0.000237101,0.0004184039],"genre_scores_gemma":[0.64139,0.00001866464,0.356335,0.001837296,0.00005040586,0.00004726391,0.0001765095,0.000008993024,0.000135814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6610751,"threshold_uncertainty_score":0.6931296,"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."}}