{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003509551,0.0004609751,0.001063232,0.0006225941,0.0002918051,0.0003808282,0.0007298774,0.0004028881,0.00360952],"category_scores_gemma":[0.0004841573,0.0002743009,0.0005302067,0.0003257376,0.0001430078,0.0009708275,0.0004567047,0.0005381817,0.00187992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002805537,"about_ca_system_score_gemma":0.0004072399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002045669,"about_ca_topic_score_gemma":0.002367287,"domain_scores_codex":[0.9996471,0.00003511066,0.0000171245,0.00009997995,0.0001587881,0.00004188808],"domain_scores_gemma":[0.9998165,0.00004349384,0.0000176771,0.00003689227,0.00007106255,0.00001445964],"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":[0.0005021573,0.0002456642,0.0028125,0.0001737448,0.0000913204,0.0002214757,0.00009388448,0.006910807,0.1372243,0.002357197,0.005356495,0.8440105],"study_design_scores_gemma":[0.00009678249,0.0009815039,0.01211277,0.00006827668,0.000274024,0.002351713,0.00008848669,0.8004979,0.162103,0.003640844,0.0176386,0.0001459908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05835766,0.001110534,0.9304094,0.0001054531,0.0001989315,0.0001450556,0.0003245732,0.005606413,0.003742003],"genre_scores_gemma":[0.5210928,0.001382614,0.4600718,0.0002183571,0.0002039447,0.0002312034,0.001221901,0.0001439789,0.01543339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00360952,"threshold_uncertainty_score":0.01207501,"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."}}