{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006323764,0.00047415,0.0006843308,0.001350552,0.0003666107,0.0006986574,0.0007245574,0.0007836933,0.00438515],"category_scores_gemma":[0.001206242,0.0001554985,0.0004248931,0.001505939,0.0005463043,0.001822366,0.0007087283,0.0008138357,0.00307846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004924353,"about_ca_system_score_gemma":0.000519471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007362015,"about_ca_topic_score_gemma":0.0004821985,"domain_scores_codex":[0.9989212,0.0002560979,0.00007968073,0.0001535142,0.0005022677,0.00008729744],"domain_scores_gemma":[0.9994832,0.00007584026,0.00004472011,0.0001470973,0.0002238622,0.00002519019],"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":[0.0002380049,0.00005595949,0.0004603986,0.0003407472,0.00005111965,0.0001269234,0.00007754157,0.007227084,0.09255896,0.08806557,0.007492318,0.8033054],"study_design_scores_gemma":[0.00009409233,0.0009088628,0.001897346,0.0001977904,0.0001247372,0.002537689,0.0001518436,0.4153377,0.2494075,0.08947775,0.2395941,0.0002707086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01124454,0.004157452,0.9747352,0.0003994885,0.0005445308,0.0001082402,0.0001839814,0.001524341,0.007102316],"genre_scores_gemma":[0.3201348,0.005809634,0.6530882,0.0005393133,0.0005446235,0.000193326,0.001089199,0.0001577545,0.01844308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00438515,"threshold_uncertainty_score":0.01466978,"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."}}