{"id":"W4381135281","doi":"10.32920/22734302.v1","title":"Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Headset; Computer science; Convolutional neural network; Facial expression; Task (project management); Virtual reality; Expression (computer science); Occlusion; Transfer of learning; Artificial intelligence; Face (sociological concept); Benchmark (surveying); Deep learning; Speech recognition; Computer vision; Engineering","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":[0.0004827931,0.001010079,0.0008168599,0.0005465499,0.0002192473,0.0005669165,0.001082583,0.0005397811,0.002749929],"category_scores_gemma":[0.001274034,0.0002754306,0.0008037461,0.0004124539,0.000334168,0.0005352125,0.0009438579,0.0008395915,0.001982427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004675394,"about_ca_system_score_gemma":0.0003852979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004921441,"about_ca_topic_score_gemma":0.005340801,"domain_scores_codex":[0.9995075,0.00008682131,0.00001630683,0.000152792,0.0001403625,0.0000961017],"domain_scores_gemma":[0.9998032,0.00004954324,0.00002190035,0.00005651975,0.00005182188,0.00001690731],"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":[0.0007604481,0.0003703627,0.003077161,0.0001455355,0.0001651271,0.0005845688,0.0001788513,0.09256694,0.07707803,0.001036697,0.01407417,0.8099621],"study_design_scores_gemma":[0.00002398984,0.0002445391,0.005384109,0.00002478871,0.00004823396,0.0004496122,0.0001065531,0.9626864,0.02635616,0.001425507,0.003216571,0.00003347505],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3193304,0.001600415,0.6550574,0.0005116513,0.0003980278,0.0003887022,0.001876928,0.008817925,0.01201846],"genre_scores_gemma":[0.8712417,0.001230535,0.1053422,0.0005249045,0.0001571855,0.0003309783,0.006357272,0.0002819002,0.01453331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004921441,"threshold_uncertainty_score":0.009785593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0778783839835256,"score_gpt":0.2982838289605843,"score_spread":0.2204054449770587,"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."}}