{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002057438,0.0001932863,0.0002227879,0.0001501755,0.0004531001,0.000239009,0.0003149495,0.0001068811,0.00001770168],"category_scores_gemma":[0.000007557771,0.0002023431,0.0001410822,0.0004257171,0.00004556261,0.0002526526,0.000005833019,0.0001866479,0.00002331837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007727754,"about_ca_system_score_gemma":0.0001272049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005699397,"about_ca_topic_score_gemma":0.00001161131,"domain_scores_codex":[0.9984261,0.0001230194,0.0003258667,0.0006046654,0.0002362247,0.0002841129],"domain_scores_gemma":[0.9985908,0.0003822708,0.0001222848,0.00053824,0.0002332748,0.0001330773],"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":[0.0001544173,0.00370989,0.0001571772,0.001015635,0.0006238268,0.0000339305,0.2227249,0.08236848,0.04860452,0.009151857,0.0007243021,0.630731],"study_design_scores_gemma":[0.0007885065,0.0001657609,0.00009377969,0.00008165831,0.00003328229,0.00001534396,0.0007070419,0.8772733,0.1157731,0.0002296527,0.004568034,0.0002705347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1557561,0.00009398378,0.8421603,0.0003087612,0.000677984,0.0007215178,0.00002404889,0.0002225217,0.00003482676],"genre_scores_gemma":[0.991331,0.000010432,0.007892725,0.0002196509,0.00004417025,0.0002580128,0.00001024021,0.00001992128,0.0002138773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8355749,"threshold_uncertainty_score":0.8251311,"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."}}