{"id":"W2949791080","doi":"10.48550/arxiv.1809.06218","title":"Facial Recognition with Encoded Local Projections","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Artificial intelligence; Pattern recognition (psychology); Histogram; Support vector machine; Local binary patterns; Computer science; Image (mathematics); Feature (linguistics); Histogram of oriented gradients; Feature vector; Construct (python library); Computer vision","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001270585,0.0002674964,0.0002320059,0.0002577615,0.000210237,0.0001056148,0.0009390864,0.0002442458,0.00002619062],"category_scores_gemma":[0.0000255142,0.0002689862,0.0001125967,0.000718057,0.0002604482,0.0007064798,0.0009105667,0.0005012558,0.0001134307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000206892,"about_ca_system_score_gemma":0.0002512492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009147247,"about_ca_topic_score_gemma":0.00005801913,"domain_scores_codex":[0.9984314,0.00008332008,0.0001428442,0.0009707793,0.00009656676,0.0002750943],"domain_scores_gemma":[0.9985067,0.00003853701,0.0001913682,0.00079378,0.0003592877,0.0001103679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002297486,0.002734585,0.006761672,0.001125212,0.001508515,0.005760234,0.003598819,0.04705809,0.001258029,0.1992892,0.01295768,0.7156505],"study_design_scores_gemma":[0.001782243,0.001859218,0.0007080715,0.0007428459,0.0002727108,0.0001052474,0.0002890911,0.320386,0.05452081,0.6080224,0.008563049,0.002748337],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02032423,0.00001208997,0.9736445,0.00003474168,0.0002321025,0.0003976698,0.00002006724,0.0007658561,0.004568736],"genre_scores_gemma":[0.9660666,0.00009751933,0.03281642,0.00007816528,0.0001228199,0.000003473024,0.00002668919,0.00001766749,0.0007706389],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9457424,"threshold_uncertainty_score":0.9999762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08794478709006447,"score_gpt":0.2153670569655661,"score_spread":0.1274222698755016,"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."}}