{"id":"W3150295303","doi":"10.18280/ts.380105","title":"Face Recognition by Using 2D Orthogonal Subspace Projections","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Support vector machine; Subspace topology; Computer science; Projection (relational algebra); Face (sociological concept); Convolutional neural network; Feature (linguistics); Matrix (chemical analysis); Feature vector; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005976352,0.0006378201,0.0007438167,0.001375271,0.0003251459,0.0008518087,0.0004463713,0.0004304063,0.002042506],"category_scores_gemma":[0.0009949579,0.0002664167,0.0008860365,0.00141384,0.0003562338,0.001275228,0.0007768018,0.0004882577,0.001156654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002456527,"about_ca_system_score_gemma":0.0004763839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001923718,"about_ca_topic_score_gemma":0.002142953,"domain_scores_codex":[0.9992108,0.0001538085,0.00002805421,0.0001846359,0.0003663105,0.00005632628],"domain_scores_gemma":[0.9996738,0.00008348906,0.00003152418,0.00007893919,0.0001183699,0.0000138762],"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":[0.0001224487,0.00007545178,0.001561123,0.0001246433,0.0001139952,0.00007562049,0.00009290123,0.04492087,0.0526204,0.006351651,0.002148471,0.8917924],"study_design_scores_gemma":[0.00001055635,0.0001558001,0.003454657,0.00001987799,0.00002920215,0.0004763171,0.00007234642,0.9518016,0.03277373,0.0050126,0.006147651,0.00004569359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02446354,0.0006005501,0.9716166,0.00007871601,0.0000765827,0.00006358715,0.0001817524,0.0008219836,0.00209684],"genre_scores_gemma":[0.2437918,0.001207792,0.7510978,0.0001010764,0.00009020705,0.0001616995,0.0007931736,0.00007718084,0.002679157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002042506,"threshold_uncertainty_score":0.006832838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04206772428975267,"score_gpt":0.2608400203598336,"score_spread":0.2187722960700809,"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."}}