{"id":"W3163747707","doi":"10.18280/ts.380209","title":"Facial Expression Recognition Using 3D Points Aware Deep Neural Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face recognition and analysis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Point cloud; Computer science; Discriminative model; Artificial intelligence; Segmentation; Pattern recognition (psychology); Representation (politics); Deep learning; Exploit; Artificial neural network; Point (geometry); Field (mathematics); Set (abstract data type); Data set; Deep neural networks; Object (grammar); Mathematics","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":[0.0002199968,0.0007976993,0.0004944637,0.0005378928,0.0001564933,0.0004039553,0.0006881278,0.0004567639,0.002631441],"category_scores_gemma":[0.0004580552,0.0002801044,0.0006011912,0.0003696284,0.0001944058,0.0004797957,0.0006506534,0.0006437494,0.001240922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005035369,"about_ca_system_score_gemma":0.000297911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005965133,"about_ca_topic_score_gemma":0.008378762,"domain_scores_codex":[0.9997513,0.00003563159,0.000007636772,0.00006661328,0.0000918903,0.00004698662],"domain_scores_gemma":[0.9998991,0.00001699808,0.000009218782,0.00001834964,0.00004776544,0.000008533634],"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":[0.0005095618,0.0002160293,0.00315136,0.00009592818,0.000135391,0.0002154996,0.0000597993,0.1265334,0.08133744,0.001627388,0.01134775,0.7747704],"study_design_scores_gemma":[0.000008421091,0.00004577387,0.001850737,0.00001238262,0.00001877773,0.00007374685,0.00002350164,0.9813949,0.01420536,0.0009592928,0.001394384,0.0000126807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2371134,0.001551374,0.7411383,0.000536861,0.0004180514,0.000169391,0.002229164,0.005290743,0.01155269],"genre_scores_gemma":[0.8525162,0.0009561153,0.1320335,0.000322994,0.00006482415,0.000139348,0.003961006,0.0001354533,0.009870619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005965133,"threshold_uncertainty_score":0.01186085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03883952465350234,"score_gpt":0.2548524865005694,"score_spread":0.216012961847067,"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."}}