{"id":"W3094151137","doi":"10.1109/bigmm50055.2020.00020","title":"Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Headset; Computer science; Convolutional neural network; Facial expression; Virtual reality; Transfer of learning; Task (project management); Artificial intelligence; Expression (computer science); Deep learning; Occlusion; Benchmark (surveying); Face (sociological concept); Facial recognition system; Speech recognition; Computer vision; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001638003,0.0001625747,0.0001941019,0.00006902481,0.0002383004,0.0001338069,0.000239715,0.0001087758,0.001343614],"category_scores_gemma":[0.00006089493,0.0001392517,0.000155074,0.0003141545,0.00002378166,0.0003313837,0.00006376098,0.0002583796,0.0005195143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002829094,"about_ca_system_score_gemma":0.00004402306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007976648,"about_ca_topic_score_gemma":0.00001809853,"domain_scores_codex":[0.9982207,0.0003177798,0.0002559447,0.0005527115,0.0004417759,0.0002110616],"domain_scores_gemma":[0.9993718,0.0001312951,0.00003650648,0.0001770404,0.00005889577,0.0002244141],"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":[0.0007291344,0.0007665464,0.0006481757,0.00002695367,0.00007640701,0.00007073695,0.003241876,0.09311187,0.2789574,0.0009912241,0.00455907,0.6168206],"study_design_scores_gemma":[0.001005305,0.0002773822,0.0003786329,0.00004399291,0.00001740191,3.679287e-7,0.0001477925,0.8673562,0.1286412,0.000417334,0.001438299,0.000276019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09675755,0.000002899704,0.8936623,0.0076522,0.0001055922,0.00009036244,0.0000242144,0.0003330878,0.001371785],"genre_scores_gemma":[0.9918205,0.000007106014,0.002319734,0.005468524,0.0001311083,0.000005709312,0.0001959631,0.000008817419,0.00004252249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.895063,"threshold_uncertainty_score":0.9995693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0516860973992504,"score_gpt":0.2656558896484443,"score_spread":0.2139697922491939,"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."}}