{"id":"W4377832625","doi":"10.18280/ts.400223","title":"Human Face and Facial Expression Recognition Using Deep Learning and SNet Architecture Integrated with BottleNeck Attention Module","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face recognition and analysis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bottleneck; Facial expression recognition; Architecture; Face (sociological concept); Artificial intelligence; Deep learning; Computer science; Expression (computer science); Facial recognition system; Facial expression; Computer architecture; Pattern recognition (psychology); Speech recognition; Embedded system; Art; Visual arts; Sociology","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":[0.0004325113,0.0005771376,0.0005819968,0.0005615419,0.0002639599,0.0004431705,0.001068646,0.00053794,0.00328224],"category_scores_gemma":[0.0004548043,0.0003041258,0.000647005,0.0003938883,0.0002405642,0.0008739446,0.0006899083,0.0007046215,0.00091323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004873523,"about_ca_system_score_gemma":0.0004615892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004361699,"about_ca_topic_score_gemma":0.006777957,"domain_scores_codex":[0.9997991,0.00002597386,0.000009079771,0.00006485052,0.00005784594,0.00004326415],"domain_scores_gemma":[0.9998719,0.00002716589,0.00001131737,0.00001911213,0.00005741374,0.00001306386],"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":[0.000633767,0.0003859408,0.002897898,0.0001420421,0.0002830486,0.0001955733,0.00008734486,0.108571,0.09202965,0.004219261,0.005996659,0.7845577],"study_design_scores_gemma":[0.000007689782,0.0001308471,0.001117353,0.000007230037,0.00003414005,0.00008826336,0.00001409458,0.976734,0.01941665,0.001404137,0.001033149,0.00001246493],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09543514,0.0006974017,0.8951004,0.0002246148,0.0001754347,0.00009490584,0.0002767543,0.003341092,0.004654224],"genre_scores_gemma":[0.7839937,0.000543367,0.1983995,0.0003424567,0.0000747962,0.0001645522,0.0008935395,0.00008513164,0.01550306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004361699,"threshold_uncertainty_score":0.01098019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232034284643986,"score_gpt":0.2488304671902783,"score_spread":0.2256270387258797,"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."}}