{"id":"W3088208260","doi":"10.1002/itl2.229","title":"Smart face identification via improved <scp>LBP</scp> and <scp>HOG</scp> features","year":2020,"lang":"en","type":"article","venue":"Internet Technology Letters","topic":"Face recognition and analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"","keywords":"Local binary patterns; Artificial intelligence; Pattern recognition (psychology); Histogram; Feature extraction; Computer science; Facial recognition system; Face (sociological concept); Feature (linguistics); Dimensionality reduction; Identification (biology); Histogram of oriented gradients; Computer vision; Fuse (electrical); Principal component analysis; Engineering; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000149189,0.0002670509,0.000301626,0.0004282082,0.00009237235,0.0002801973,0.001190471,0.0002749412,0.000005651863],"category_scores_gemma":[0.0003799826,0.0002646386,0.0001201159,0.0007298716,0.0002652305,0.0003689337,0.0004877983,0.0005143909,0.0003200374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003990354,"about_ca_system_score_gemma":0.00001469813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004176312,"about_ca_topic_score_gemma":0.00002765726,"domain_scores_codex":[0.9981475,0.00006019418,0.0003512158,0.0008191567,0.0001999497,0.0004219201],"domain_scores_gemma":[0.998924,0.0001438261,0.0002156357,0.0004985037,0.00007619154,0.0001418532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001770503,0.00006800373,0.003667753,0.00005364122,0.0002955151,0.00008026987,0.002408563,0.0000258256,0.8405111,0.001783381,0.09197294,0.05913127],"study_design_scores_gemma":[0.001434608,0.0003080917,0.005625411,0.00008698119,0.0001651237,0.0002614271,0.001438652,0.1214917,0.7912005,0.002639828,0.07503156,0.0003161042],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4352134,0.0002992478,0.5210329,0.04183025,0.0002380582,0.0002031723,0.000005641321,0.0009840954,0.0001931596],"genre_scores_gemma":[0.9847022,0.0000447422,0.004260114,0.01001964,0.00005067708,0.00004415876,0.00001612961,0.00002092829,0.000841444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5494887,"threshold_uncertainty_score":0.9999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008085983481000673,"score_gpt":0.2097413023144098,"score_spread":0.2016553188334092,"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."}}