{"id":"W2787191314","doi":"10.1007/978-3-642-39479-9_46","title":"Illumination Invariant Face Recognition","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Fast Fourier transform; Facial recognition system; Complex wavelet transform; Face (sociological concept); Pattern recognition (psychology); Computer vision; Invariant (physics); Classifier (UML); Face detection; Three-dimensional face recognition; Wavelet; Wavelet transform; Discrete wavelet transform; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002083385,0.0006965051,0.000601655,0.0008699715,0.0002962188,0.0007865477,0.001203508,0.0006824684,0.02248607],"category_scores_gemma":[0.0002995986,0.000329641,0.000549113,0.0007935839,0.0002930265,0.0008063501,0.0008144602,0.000747042,0.02515704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002865272,"about_ca_system_score_gemma":0.0002991867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007657486,"about_ca_topic_score_gemma":0.001370938,"domain_scores_codex":[0.9997122,0.00001603105,0.00001062232,0.00008354077,0.0001379841,0.00003963343],"domain_scores_gemma":[0.9998595,0.00001507168,0.000008138195,0.00005925518,0.00004952731,0.000008476988],"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":[0.00004262853,0.00005295203,0.0001341697,0.00009828036,0.00001746552,0.00004997673,0.00001602419,0.001456269,0.07440038,0.004124375,0.02268938,0.8969181],"study_design_scores_gemma":[0.00001760132,0.000217661,0.005365552,0.0001330989,0.00009772473,0.00265496,0.0000759706,0.1206833,0.4905172,0.01468048,0.3654565,0.00009998116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008779741,0.007598406,0.878019,0.000255392,0.0008445894,0.0001277415,0.0007060801,0.009187659,0.09448139],"genre_scores_gemma":[0.1363739,0.009656125,0.4949366,0.0008904878,0.0005155894,0.0001654181,0.006199175,0.001429206,0.3498335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02248607,"threshold_uncertainty_score":0.07522339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369699591984155,"score_gpt":0.2337593971038833,"score_spread":0.2100624011840418,"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."}}