{"id":"W2119537429","doi":"10.1109/tsmcb.2009.2037131","title":"An Analysis of Random Projection for Changeable and Privacy-Preserving Biometric Verification","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Random projection; Computer science; Independent and identically distributed random variables; Data mining; Curse of dimensionality; Software deployment; Projection (relational algebra); Feature (linguistics); Random variable; Pattern recognition (psychology); Similarity (geometry); Feature vector; Information privacy; Gaussian; Domain (mathematical analysis); Artificial intelligence; Algorithm; Image (mathematics); Mathematics; Computer security; Statistics","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":[0.002372556,0.0005999669,0.0006908522,0.0006433422,0.0003883316,0.0009675412,0.0009479126,0.0008176792,0.002397187],"category_scores_gemma":[0.009525499,0.0004526139,0.0008490026,0.0007066423,0.001558467,0.001814966,0.001166285,0.001154454,0.0005857797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005481009,"about_ca_system_score_gemma":0.0005693383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003142549,"about_ca_topic_score_gemma":0.0002015736,"domain_scores_codex":[0.997136,0.001178254,0.000105331,0.000328124,0.001145992,0.0001062719],"domain_scores_gemma":[0.9957729,0.002790698,0.0003044221,0.0006611529,0.0003998566,0.000071025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000253566,0.0001065644,0.0007142242,0.0005456634,0.0001094644,0.0004896756,0.0002602476,0.2319108,0.04146624,0.4439182,0.001783163,0.2784423],"study_design_scores_gemma":[0.00001312906,0.0001659547,0.0004266801,0.00003115361,0.00002809182,0.0005523016,0.00002264741,0.9310452,0.01485048,0.04903775,0.00379453,0.00003206951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00453945,0.0003903907,0.99371,0.00006655247,0.000015931,0.00002548164,0.000008389005,0.00005883668,0.001184953],"genre_scores_gemma":[0.4437427,0.002654271,0.5478336,0.0001350877,0.0002339481,0.0002117761,0.0001150742,0.0001341664,0.004939403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002397187,"threshold_uncertainty_score":0.01254743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0293628581158245,"score_gpt":0.2740160634878878,"score_spread":0.2446532053720633,"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."}}