AU-inspired Deep Networks for Facial Expression Feature LearningMengyi Liu, Shaoxin Li, Shiguang Shan, Xilin Chen2015-02-27articleNeurocomputing224 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 — 224 citations exist upstream.References1978Facial Action Coding System2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions2002Comprehensive database for facial expression analysis2011Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark2006Dynamics of facial expression extracted automatically from video2013AU-aware Deep Networks for facial expression recognitionCited by2019Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition2017Facial Expression Recognition Using Weighted Mixture Deep Neural Network Based on Double-Channel Facial Images2018Driver’s Facial Expression Recognition in Real-Time for Safe Driving2017Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition2019Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy2020A Deeper Look at Facial Expression Dataset Bias2017Facial Expression Recognition Using a Hybrid CNN–SIFT Aggregator2017Deep spatial-temporal feature fusion for facial expression recognition in static images