{"id":"W4239514472","doi":"10.7763/ijmlc.2015.v5.542","title":"An Improved Histogram-Based Features in Low-Frequency DCT Domain for Face Recognition","year":2015,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Computing","topic":"Optical Systems and Laser Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Computer science; Discrete cosine transform; Histogram; Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Face (sociological concept); Computer vision; Domain (mathematical analysis); Frequency domain; Speech recognition; Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003502023,0.0003302881,0.0004886285,0.0009134776,0.0001702424,0.0003714079,0.0005573231,0.0003178307,0.002064476],"category_scores_gemma":[0.001042602,0.0001591673,0.0003912889,0.001005806,0.0001987447,0.0007444088,0.0003516466,0.0004098412,0.0006990443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650672,"about_ca_system_score_gemma":0.0003680421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001996483,"about_ca_topic_score_gemma":0.00209074,"domain_scores_codex":[0.99962,0.0000500528,0.0000233408,0.00006741625,0.0002098083,0.00002931864],"domain_scores_gemma":[0.9997361,0.00006926342,0.00002275397,0.00003356657,0.0001262423,0.00001204176],"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":[0.0002414563,0.00009839127,0.001046397,0.0001274376,0.00003697549,0.00008693962,0.00003314196,0.03551773,0.1256861,0.006582235,0.003847603,0.8266956],"study_design_scores_gemma":[0.00003248073,0.0001881689,0.003837146,0.00001715696,0.00003696942,0.0003977329,0.00002290703,0.915463,0.07108558,0.002627018,0.006251556,0.00004021264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01641085,0.0004927575,0.9810642,0.00008050488,0.00007484754,0.0000589501,0.0001474366,0.0005374067,0.001133012],"genre_scores_gemma":[0.31792,0.0006541567,0.6759987,0.0001452671,0.0001114776,0.0001501676,0.0007300667,0.0001333968,0.004156681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002064476,"threshold_uncertainty_score":0.006906331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008116222366048788,"score_gpt":0.2547538177559776,"score_spread":0.2466375953899288,"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."}}