{"id":"W2993949519","doi":"10.9781/ijimai.2016.412","title":"Offline Face Recognition System Based on Gabor- Fisher Descriptors and Hidden Markov Models","year":2016,"lang":"en","type":"article","venue":"International Journal of Interactive Multimedia and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Pattern recognition (psychology); Hidden Markov model; Artificial intelligence; Linear discriminant analysis; Facial recognition system; Gabor wavelet; Face (sociological concept); Segmentation; Curse of dimensionality; Wavelet; Wavelet transform; Discrete wavelet transform","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003305681,0.0001714944,0.00019789,0.0003392914,0.00006193589,0.0002045737,0.0004178911,0.00007258272,0.00006338109],"category_scores_gemma":[0.0003157559,0.0001147449,0.00008251783,0.00008907965,0.00009139422,0.001402289,0.0000921029,0.0001880339,0.0000429577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097064,"about_ca_system_score_gemma":0.00004921007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002204378,"about_ca_topic_score_gemma":0.000005614898,"domain_scores_codex":[0.9984689,0.0001186263,0.0005495274,0.0002812982,0.0004217824,0.0001598609],"domain_scores_gemma":[0.997824,0.0007038147,0.000398095,0.0001162696,0.000807797,0.0001500286],"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":[0.0004958832,0.0001329803,0.0001142903,0.000007807756,0.00005508853,0.00005494789,0.0006775424,0.00008676977,0.01437964,0.0005117595,0.0002689293,0.9832144],"study_design_scores_gemma":[0.0005344535,0.0005701638,0.0004265474,0.0023979,0.00002663883,0.0001995465,0.001509885,0.7026551,0.2780031,0.01290257,0.000380183,0.0003938984],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1718292,0.0000527269,0.8213239,0.003710431,0.002344267,0.0001332535,0.0000382009,0.00003478139,0.0005331899],"genre_scores_gemma":[0.9793897,0.00009778985,0.01988337,0.0003063147,0.0002633043,0.000007281617,0.000003254792,0.000009602179,0.00003933673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9828205,"threshold_uncertainty_score":0.4679162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04708530327207906,"score_gpt":0.2831041318276288,"score_spread":0.2360188285555497,"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."}}