{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001738211,0.0001394804,0.0001198093,0.00007365482,0.0002552497,0.0001905202,0.0001818162,0.00006516015,0.0006408766],"category_scores_gemma":[0.00001469993,0.0001406987,0.00008040806,0.0003993605,0.00002385294,0.0006136717,0.00008481604,0.0001333836,0.0001249444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005105721,"about_ca_system_score_gemma":0.0001246376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001866444,"about_ca_topic_score_gemma":0.00001321502,"domain_scores_codex":[0.9986537,0.000113375,0.0002245851,0.0003999707,0.0003417915,0.0002665532],"domain_scores_gemma":[0.9994338,0.00004575529,0.00008240779,0.000173838,0.0001660413,0.00009815252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004250377,0.001064328,0.0007884633,0.0000726768,0.0001185481,0.0001007938,0.002042869,0.001094613,0.7451165,0.0008905947,0.05262186,0.1960462],"study_design_scores_gemma":[0.003008488,0.0003521809,0.001313383,0.0003963273,0.0001074129,0.0003624219,0.001700879,0.2680699,0.6766778,0.003455141,0.04317754,0.00137861],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3171681,0.0001099776,0.6800305,0.0009937556,0.0002725319,0.0001898566,0.0000455545,0.0001573081,0.001032377],"genre_scores_gemma":[0.9656956,0.00004568225,0.03226029,0.0009188483,0.0001710872,0.00005862979,0.0002359874,0.00001783148,0.0005960225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6485275,"threshold_uncertainty_score":0.701715,"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."}}