{"id":"W2152404412","doi":"10.1109/cvprw.2014.96","title":"Fast LBP Face Detection on Low-Power SIMD Architectures","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"SIMD; Video Graphics Array; Computer science; Face detection; Reuse; Face (sociological concept); Parallelism (grammar); Exploit; Artificial intelligence; Feature extraction; Parallel computing; Facial recognition system; Embedded system; Field-programmable gate array","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":[0.0005487303,0.000127329,0.0001266539,0.0000931485,0.0001075462,0.0001207723,0.0004440606,0.00005496593,0.00001875948],"category_scores_gemma":[0.0001293625,0.00009739132,0.00006526744,0.0002204838,0.00002497982,0.00008404587,0.00008157134,0.0001592927,0.0002038072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001459491,"about_ca_system_score_gemma":0.00001023807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002370866,"about_ca_topic_score_gemma":0.00005329697,"domain_scores_codex":[0.9988552,0.0001967612,0.0001289704,0.0003548899,0.0002224958,0.0002416602],"domain_scores_gemma":[0.999032,0.0002748422,0.00004414613,0.0005476832,0.00003254878,0.00006879125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001163003,0.00004536507,0.0004151322,0.000007729989,0.0000106562,0.000001973851,0.0004700517,0.005442925,0.00452354,0.006553009,0.0002267859,0.9822912],"study_design_scores_gemma":[0.001444955,0.0018587,0.1411051,0.00008426859,0.000007619217,0.00005919594,0.00007097215,0.1971487,0.5727769,0.03792001,0.04617585,0.001347797],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08292125,0.00000628278,0.8876693,0.0003463665,0.0005105115,0.00006703726,2.793643e-7,0.0003106828,0.02816834],"genre_scores_gemma":[0.9752648,8.73292e-7,0.02319057,0.00103155,0.00008028193,0.000005114624,2.481185e-7,0.000008325494,0.0004181962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9809434,"threshold_uncertainty_score":0.3971502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009808355271883817,"score_gpt":0.2576159805947832,"score_spread":0.2478076253228994,"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."}}