{"id":"W2905558153","doi":"10.1364/ao.57.010305","title":"Double random phase encoding for cancelable face and iris recognition","year":2018,"lang":"en","type":"article","venue":"Applied Optics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Biometrics; Computer science; Iris recognition; Artificial intelligence; Encryption; Face (sociological concept); Feature (linguistics); Pattern recognition (psychology); IRIS (biosensor); Encoding (memory); Feature extraction; Computer vision; Computer security","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":[0.0003011996,0.0003961297,0.0002171641,0.0003625899,0.0001769698,0.0003817263,0.0004022754,0.0004286171,0.00226556],"category_scores_gemma":[0.001115632,0.0001169091,0.0002591167,0.0003948008,0.0002688309,0.0007238745,0.0003111401,0.0004539973,0.0008264623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002791974,"about_ca_system_score_gemma":0.0003047144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003946541,"about_ca_topic_score_gemma":0.0005081652,"domain_scores_codex":[0.9995306,0.0001014918,0.00002585675,0.00006366624,0.0002484629,0.00003001899],"domain_scores_gemma":[0.9996006,0.0001258385,0.0000766342,0.00008850447,0.00009604276,0.00001242733],"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":[0.0005750522,0.00009682157,0.0006640872,0.0003075331,0.00004050896,0.0004252522,0.0001168075,0.01555267,0.3138936,0.07016933,0.003495191,0.594663],"study_design_scores_gemma":[0.00009068337,0.0006869969,0.001379278,0.00009223635,0.00009511306,0.003052366,0.00004530445,0.3856867,0.5313739,0.01496225,0.06241896,0.0001161683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03015331,0.001668106,0.9604163,0.0002815507,0.0002284021,0.00009792455,0.00007907056,0.0008407756,0.006234593],"genre_scores_gemma":[0.4674708,0.001670042,0.5173561,0.0003058094,0.0001686063,0.0001261283,0.0002992915,0.00008939519,0.01251373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00226556,"threshold_uncertainty_score":0.007579088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05569695132514568,"score_gpt":0.3038091403995686,"score_spread":0.248112189074423,"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."}}