{"id":"W2130637684","doi":"10.1007/11559573_57","title":"Image Space I 3 and Eigen Curvature for Illumination Insensitive Face Detection","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Curvature; Face (sociological concept); Brightness; Image (mathematics); Facial recognition system; Image processing; Principal component analysis; Face detection; Space (punctuation); Pattern recognition (psychology); Optics; Mathematics; Physics; Geometry","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":[0.0004191983,0.0008135798,0.0007318622,0.001033066,0.0003491367,0.00100063,0.001081248,0.0009366338,0.0047363],"category_scores_gemma":[0.001578993,0.0004859063,0.0009035208,0.001155931,0.0007394997,0.001439382,0.001163697,0.001158449,0.002909437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003978792,"about_ca_system_score_gemma":0.0003746593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002289199,"about_ca_topic_score_gemma":0.002440459,"domain_scores_codex":[0.9995203,0.00009500975,0.00002576122,0.0001104653,0.0002103658,0.00003803473],"domain_scores_gemma":[0.9995478,0.0000993237,0.00004440764,0.0001807163,0.0001080049,0.00001972074],"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":[0.0002350824,0.00009650164,0.000688414,0.0002395878,0.00006134128,0.0001794777,0.0001625298,0.03797292,0.07487606,0.08587726,0.01345215,0.7861587],"study_design_scores_gemma":[0.00000674672,0.00008434177,0.001812783,0.00003099572,0.00002064819,0.0005430586,0.00005853181,0.8993716,0.03268953,0.05481548,0.01050792,0.00005838837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004674286,0.0007038924,0.9911341,0.0001015234,0.00006761086,0.00002232619,0.00008161713,0.001188174,0.002026433],"genre_scores_gemma":[0.1779505,0.001674303,0.8009179,0.000184591,0.0001773768,0.0001323294,0.0006892658,0.0009163842,0.01735727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0047363,"threshold_uncertainty_score":0.01584452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01124280104529312,"score_gpt":0.2372906760310131,"score_spread":0.22604787498572,"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."}}