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Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition

Mengyi Liu, Shiguang Shan, Ruiping Wang, Xilin Chen
2016 IEEE Transactions on Image Processing  
For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account.  ...  In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e. expressionlet.  ...  Manifold 1 Manifold N Universal Manifold Model (UMM) Manifold i Input video i Expressionlets Video 1 Manifold 2 Video 2 Video N TABLE I THE I NUMBER OF SAMPLES FOR EACH EXPRESSION IN  ... 
doi:10.1109/tip.2016.2615424 pmid:28113507 fatcat:4s2eov6hjfesxjy3gtzmjo24zu

Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition

Mengyi Liu, Shiguang Shan, Ruiping Wang, Xilin Chen
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account.  ...  In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e. expressionlet.  ...  Conclusion In this paper, we propose a new method for dynamic facial expression recognition.  ... 
doi:10.1109/cvpr.2014.226 dblp:conf/cvpr/LiuSWC14 fatcat:dmsgdl25b5cyjfni6t7drvxgae

Exemplar Hidden Markov Models for classification of facial expressions in videos

Karan Sikka, Abhinav Dhall, Marian Bartlett
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
However, these approaches don't fully exploit the temporal dynamics of facial expressions. Hidden Markov Models (HMMs), provide a method for modeling variable-length expression timeseries.  ...  Facial expressions are dynamic events comprised of meaningful temporal segments.  ...  This paper explored an approach for combining the modeling strength of HMMs with the discriminative power of SVMs via probabilistic kernels for the task of facial expression recognition.  ... 
doi:10.1109/cvprw.2015.7301350 dblp:conf/cvpr/SikkaDB15 fatcat:bwaxxpbvqvaxlp3x7ekzguipee

Negative Emotions Sensitive Humanoid Robot with Attention-Enhanced Facial Expression Recognition Network

Rongrong Ni, Xiaofeng Liu, Yizhou Chen, Xu Zhou, Huili Cai, Loo Chu Kiong
2022 Intelligent Automation and Soft Computing  
Chen, “Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition,” in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, OH, USA  ...  Zhang et al. [32] proposed an end-to-end deep learning model, exploiting different poses and expressions jointly for simultaneous facial image synthesis and pose-invariant facial expression recognition  ... 
doi:10.32604/iasc.2022.026813 fatcat:xqh3mo56izbqzbjfe7hlswmsua

A Multi-Column CNN Model for Emotion Recognition from EEG Signals

Heekyung Yang, Jongdae Han, Kyungha Min
2019 Sensors  
We present a multi-column CNN-based model for emotion recognition from EEG signals.  ...  We apply the model to EEG signals from DEAP dataset for comparison and demonstrate the improved accuracy of our model.  ...  Acknowledgments: We appreciate Euichul Lee for his valuable advices. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s19214736 pmid:31683608 pmcid:PMC6865186 fatcat:5z647sxn7raixkhmzjaa45jtcu

Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization

Baohan Xu, Yanwei Fu, Yu-Gang Jiang, Boyang Li, Leonid Sigal
2018 IEEE Transactions on Affective Computing  
textual corpora for zero-shot recognition of emotion classes unseen during training.  ...  Specifically, our framework (1) learns a video encoding from an auxiliary emotional image dataset in order to improve supervised video emotion recognition, and (2) transfers knowledge from an auxiliary  ...  ACKNOWLEDGMENTS The authors would like to thank Chong-Wah Ngo for his constructive advise.  ... 
doi:10.1109/taffc.2016.2622690 fatcat:huwcluofnnesfctwyknafpqd74