{"id":"W1994670097","doi":"10.1109/cibim.2014.7015449","title":"Adaptive multi-stream score fusion for illumination invariant face recognition","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Complex wavelet transform; Computer science; Color constancy; Normalization (sociology); Wavelet transform; Computer vision; Weighting; Biometrics; Wavelet; Face (sociological concept); Robustness (evolution); Invariant (physics); Mathematics; Discrete wavelet transform; Image (mathematics)","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":[0.000306595,0.0001206038,0.0001118346,0.0001060169,0.0001702892,0.00009160935,0.0002414184,0.00009448172,0.00004442737],"category_scores_gemma":[0.0001411874,0.0001008529,0.00006083328,0.0001612964,0.00002068339,0.0006892094,0.00009150142,0.0000663476,0.0002384035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002532019,"about_ca_system_score_gemma":0.00002162746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003691067,"about_ca_topic_score_gemma":0.000039011,"domain_scores_codex":[0.9990196,0.00006825124,0.0001836871,0.0003664457,0.0001670137,0.0001950576],"domain_scores_gemma":[0.9992357,0.0001443202,0.0000971214,0.0002281887,0.0002178528,0.00007676965],"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":[0.00003405728,0.0001810835,0.00003845676,0.00002308685,0.000008547199,8.304427e-7,0.0007232535,0.00005792952,0.01607987,0.004767026,0.004596434,0.9734894],"study_design_scores_gemma":[0.001472809,0.0004022714,0.001082361,0.0001305751,0.00001107236,0.000006431868,0.0002997198,0.890861,0.09284846,0.00968891,0.002866324,0.0003300877],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01438915,0.000008227356,0.9817958,0.0006000195,0.0002874066,0.0004308442,0.00001030655,0.0001983602,0.002279873],"genre_scores_gemma":[0.6373273,0.00001425798,0.3607811,0.0006385879,0.00008185102,0.0001241248,0.0001029171,0.00001058086,0.0009192674],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9731593,"threshold_uncertainty_score":0.4112662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06051294866077118,"score_gpt":0.2627607680725254,"score_spread":0.2022478194117542,"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."}}