{"id":"W2013415492","doi":"10.1155/2009/260148","title":"Sorted Index Numbers for Privacy Preserving Face Recognition","year":2009,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Facial recognition system; Face (sociological concept); Index (typography); Set (abstract data type); Pattern recognition (psychology); Transformation (genetics); Artificial intelligence; Projection (relational algebra); Feature (linguistics); Matching (statistics); Random projection; Data mining; Algorithm; Mathematics","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":[0.0008281553,0.0004579552,0.000531184,0.001007746,0.0005507628,0.001245205,0.0009011517,0.0005658398,0.003907404],"category_scores_gemma":[0.003637571,0.0001942526,0.0003788612,0.001344184,0.0009543202,0.002319262,0.0009865373,0.0008465931,0.001885393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005015148,"about_ca_system_score_gemma":0.0006193943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002984694,"about_ca_topic_score_gemma":0.0003359985,"domain_scores_codex":[0.9985043,0.0002616988,0.0001183732,0.000234756,0.0007942436,0.00008665198],"domain_scores_gemma":[0.9986147,0.0004056352,0.0001824991,0.0005259306,0.0002240587,0.00004724081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005777446,0.00007766792,0.0008052525,0.0002449552,0.0000327852,0.000252714,0.0002135182,0.02276212,0.0650548,0.1763945,0.004476583,0.7291073],"study_design_scores_gemma":[0.00009027117,0.0006195782,0.001346021,0.000102757,0.00006784004,0.002512934,0.0001707968,0.4482218,0.1867189,0.2483841,0.1116183,0.000146751],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01545671,0.0008335165,0.9760576,0.0001678748,0.0001512713,0.00007069326,0.0001200442,0.0007280086,0.006414185],"genre_scores_gemma":[0.2860469,0.001233075,0.7037434,0.0002509761,0.0002750871,0.0002047667,0.0004553609,0.0001461602,0.007644286],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003907404,"threshold_uncertainty_score":0.0130716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03888488282297544,"score_gpt":0.3285459080691336,"score_spread":0.2896610252461582,"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."}}