{"id":"W2077389880","doi":"10.1109/btas.2009.5339014","title":"Biometric authentication using augmented face and random projection","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Random projection; Subspace topology; Face (sociological concept); Authentication (law); Artificial intelligence; Projection (relational algebra); Computer vision; Similarity (geometry); Facial recognition system; Domain (mathematical analysis); Pattern recognition (psychology); Image (mathematics); Computer security; Mathematics; Algorithm","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":[0.0006488913,0.0005242729,0.0009996921,0.0006415197,0.0003034102,0.0006934112,0.0009564502,0.0008535393,0.001563307],"category_scores_gemma":[0.002036396,0.0003276964,0.000862383,0.0007970253,0.0008432978,0.001971518,0.001158272,0.000783525,0.001194087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002297699,"about_ca_system_score_gemma":0.000302383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004139235,"about_ca_topic_score_gemma":0.0003109835,"domain_scores_codex":[0.9983317,0.0004236187,0.00006559512,0.0003299977,0.0007653966,0.00008368638],"domain_scores_gemma":[0.9990337,0.0002074322,0.0001331168,0.0004181547,0.0001722697,0.0000352295],"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":[0.0009177748,0.0001190917,0.001642151,0.0003326139,0.0001758543,0.000926671,0.0003128345,0.0921758,0.2088698,0.0612603,0.00217404,0.6310931],"study_design_scores_gemma":[0.0000465775,0.000681074,0.002204778,0.00004493649,0.0001117161,0.004362007,0.00009368391,0.8405737,0.1166489,0.02182599,0.01320292,0.0002036983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02428537,0.0003963528,0.9730493,0.00009632199,0.00007048647,0.00003382404,0.00004096883,0.0004928589,0.001534526],"genre_scores_gemma":[0.5701137,0.0007458926,0.4230021,0.0001250865,0.0001288742,0.00008851694,0.000165997,0.00007323057,0.005556631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001563307,"threshold_uncertainty_score":0.005229771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03300636142813996,"score_gpt":0.2876591288554343,"score_spread":0.2546527674272944,"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."}}