{"id":"W3195136400","doi":"10.1109/tmc.2021.3106256","title":"Lightweight and Secure Face-based Active Authentication for Mobile Users","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biometrics; Authentication (law); Mobile device; Overhead (engineering); Cloud computing; Smart card; Embedded system; Computer network; Computer security; Operating system","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":[0.0003126802,0.0004487365,0.0004976848,0.0004917833,0.0005467578,0.0005699011,0.001067705,0.0006033584,0.003150121],"category_scores_gemma":[0.0008883127,0.0001912328,0.0003456713,0.0003287228,0.0002338025,0.001441677,0.001397361,0.0004802892,0.001667547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003979911,"about_ca_system_score_gemma":0.0005260993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002125661,"about_ca_topic_score_gemma":0.002020247,"domain_scores_codex":[0.9995013,0.00006653416,0.0000264771,0.00009287845,0.0002179681,0.00009490181],"domain_scores_gemma":[0.9995481,0.00004948759,0.00004771618,0.0001731418,0.0001448527,0.00003660023],"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":[0.00146256,0.0004067181,0.005084473,0.0002065086,0.00008924319,0.0006106627,0.0002831008,0.02008325,0.2479967,0.009395459,0.0120672,0.7023141],"study_design_scores_gemma":[0.00008184707,0.0005822901,0.006717162,0.00003621338,0.00006053458,0.001025442,0.0001176812,0.8540786,0.117922,0.003779668,0.01551378,0.00008482445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1847111,0.001067037,0.7929667,0.0002572772,0.0002179505,0.0003271577,0.0003102561,0.01330234,0.006840204],"genre_scores_gemma":[0.9400616,0.0002027626,0.05501544,0.0001284199,0.00004423663,0.0000874714,0.0002298677,0.00004794592,0.004182119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003150121,"threshold_uncertainty_score":0.01053828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567160466777199,"score_gpt":0.2624049780005392,"score_spread":0.2467333733327673,"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."}}