{"id":"W1947798063","doi":"10.1109/cscwd.2015.7230995","title":"Vector signature for face recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Facial recognition system; Signature (topology); Face (sociological concept); Bandwidth (computing); Artificial intelligence; Pattern recognition (psychology); Computer vision; Face detection; Signature recognition; Scheme (mathematics); Feature extraction; Mathematics; Computer network","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":[0.0002256871,0.00003521813,0.00003926124,0.00008617721,0.00002902004,0.00009693622,0.0002188938,0.0000455294,0.00002562345],"category_scores_gemma":[0.0001196364,0.00002990282,0.00002408498,0.0004651324,0.000007674714,0.0002151267,0.00003067932,0.00003443302,0.0002123377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000196577,"about_ca_system_score_gemma":0.00003636707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000088432,"about_ca_topic_score_gemma":0.000005097442,"domain_scores_codex":[0.9995778,0.00001441714,0.0000675824,0.0001477631,0.0001131277,0.00007931938],"domain_scores_gemma":[0.999542,0.00003466997,0.00002368719,0.0001615464,0.0001686701,0.00006941686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009128798,0.0001186466,0.00002325934,0.00001412575,0.00001055577,8.784852e-7,0.000920835,0.000002687438,0.001559596,0.06582642,0.4730598,0.4584541],"study_design_scores_gemma":[0.00123618,0.0001575297,0.000983617,0.000005636086,0.000006497599,0.000007869191,0.0002191035,0.05064057,0.04092376,0.04747755,0.8579787,0.0003630037],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001156254,0.00009288961,0.9911475,0.002494353,0.0005473195,0.0001382485,0.000007448239,0.0001282614,0.004287732],"genre_scores_gemma":[0.8154156,0.000004476743,0.1716299,0.001359334,0.0001107788,0.00003735874,0.00006020787,0.000005329419,0.01137701],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8195176,"threshold_uncertainty_score":0.2729242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09515877888085496,"score_gpt":0.2889654981280095,"score_spread":0.1938067192471545,"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."}}