{"id":"W2170519129","doi":"10.1109/icassp.2009.4959726","title":"Face recognition with enhanced privacy protection","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Facial recognition system; Computer science; Biometrics; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Similarity (geometry); Identification (biology); Set (abstract data type); Transformation (genetics); Index (typography); Three-dimensional face recognition; Similarity measure; Data mining; Computer vision; Face detection; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001038137,0.00004859162,0.00004386813,0.0001369124,0.00006587971,0.0001241378,0.0002061829,0.00002963419,0.00003002657],"category_scores_gemma":[0.00002125193,0.00003737925,0.00001398033,0.0009831548,0.000008306429,0.0004110387,0.00001405571,0.00005546967,0.0001720368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002006366,"about_ca_system_score_gemma":0.00001823794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001123733,"about_ca_topic_score_gemma":0.000002498988,"domain_scores_codex":[0.999459,0.00002096632,0.0000831948,0.0001967861,0.0001492767,0.00009073687],"domain_scores_gemma":[0.9996024,0.000005625149,0.00004161972,0.000229081,0.00008550839,0.00003575556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006363161,0.00008342494,0.000002506315,0.000002673221,0.000002484608,5.800858e-7,0.0003746345,0.000001825055,0.01049659,0.004361862,0.000296451,0.9843706],"study_design_scores_gemma":[0.001290079,0.0009162941,0.03117726,0.00003283856,0.000007718825,0.00004432356,0.00009694548,0.03237037,0.8845334,0.02541397,0.02348678,0.0006300494],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02296025,0.000006912476,0.9683952,0.002051309,0.00006179175,0.0001980724,2.403682e-7,0.0002303008,0.006095895],"genre_scores_gemma":[0.9594317,0.000003719075,0.0393475,0.0003519223,0.00001443593,0.000009341409,0.000002572424,0.000001222111,0.0008376081],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9837406,"threshold_uncertainty_score":0.2211242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03509652935908401,"score_gpt":0.2452686774156386,"score_spread":0.2101721480565545,"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."}}