{"id":"W2786433123","doi":"10.1109/dcoss.2017.19","title":"A Privacy Enhanced Facial Recognition Access Control System Using Biometric Encryption","year":2017,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Biometrics; Encryption; Computer science; Computer security; Information privacy; Key (lock); Access control; Scheme (mathematics); Internet privacy","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":[0.000444677,0.0002848282,0.0005679655,0.0003395445,0.0004200247,0.0006531638,0.0008912147,0.0006998949,0.003560492],"category_scores_gemma":[0.0005433086,0.0001380163,0.0003254653,0.0002186864,0.0002271594,0.001059636,0.0007292653,0.0004810988,0.001205773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003834336,"about_ca_system_score_gemma":0.0003281742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001082347,"about_ca_topic_score_gemma":0.0007366601,"domain_scores_codex":[0.9994639,0.00005317687,0.00003909826,0.0001254328,0.0002408819,0.0000775378],"domain_scores_gemma":[0.9997759,0.00002977681,0.00003551278,0.00005438922,0.00008116322,0.00002320697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001184527,0.0004429723,0.00279318,0.0001913975,0.0000792233,0.002000021,0.0003168005,0.004049844,0.6165448,0.009467648,0.006728024,0.3562016],"study_design_scores_gemma":[0.0002960639,0.001502756,0.01008517,0.00007428062,0.0002082827,0.01107087,0.0001502592,0.4218511,0.5025494,0.003494407,0.04847242,0.0002448811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2329352,0.001007195,0.7361068,0.0008609332,0.0003561445,0.0005244784,0.0002905051,0.01021596,0.01770279],"genre_scores_gemma":[0.9153084,0.0003097487,0.0688473,0.0002817418,0.00006380612,0.0001352103,0.0001976148,0.0000591793,0.01479708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003560492,"threshold_uncertainty_score":0.01191097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1114422848919805,"score_gpt":0.3413693343964619,"score_spread":0.2299270495044815,"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."}}