Facial Expression Recognition with Inconsistently Annotated DatasetsJiabei Zeng, Shiguang Shan, Xilin Chen2018-01-01otherLecture notes in computer science355 citationsclosedNo readable copy stored yet — this record is metadata only.Emotion and Mood RecognitionFace recognition and analysisFace and Expression RecognitionAbstractNo abstract was published for this record.Citations in this libraryStored only when both papers are in the corpus — 355 citations exist upstream.References2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild1979Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm2017Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild2015Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition2016EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild2011Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmarkCited by2021TransFER: Learning Relation-aware Facial Expression Representations with Transformers2019Local Learning With Deep and Handcrafted Features for Facial Expression Recognition2021Facial Expression Recognition With Visual Transformers and Attentional Selective Fusion2021Robust Lightweight Facial Expression Recognition Network with Label Distribution Training2021Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition2020Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition2021Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition2021Adaptively Learning Facial Expression Representation via C-F Labels and Distillation