Emotion recognition model based on facial expressions

2021 ◽  
Author(s):  
Satya Prakash Yadav
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Recognition Model ◽  
Model Based

Emotion recognition model based on the Dempster–Shafer evidence theory

2020 ◽  
Vol 29 (02) ◽  
pp. 1
Author(s):  
Qihua Xu ◽  
Chunyue Zhang ◽  
Bo Sun
Keyword(s):  
Emotion Recognition ◽  
Evidence Theory ◽  
Recognition Model ◽  
Model Based

Speech emotion recognition model based on Bi-GRU and Focal Loss

2020 ◽  
Vol 140 ◽  
pp. 358-365
Author(s):  
Zijiang Zhu ◽  
Weihuang Dai ◽  
Yi Hu ◽  
Junshan Li
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Recognition Model ◽  
Model Based

scholarly journals SCEP—A New Image Dimensional Emotion Recognition Model Based on Spatial and Channel-Wise Attention Mechanisms

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 25278-25290
Author(s):  
Bo Li ◽  
Hui Ren ◽  
Xuekun Jiang ◽  
Fang Miao ◽  
Feng Feng ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Recognition Model ◽  
Model Based

GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

2021 ◽  
Author(s):  
Jian Zhao ◽  
ZhiWei Zhang ◽  
Jinping Qiu ◽  
Lijuan Shi ◽  
Zhejun KUANG ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Rapid Development ◽  
Semantic Features ◽  
Hemispheric Differences ◽  
Recognition Model ◽  
Model Based ◽  
Spatial Features ◽  
Convolution Kernels

Abstract With the rapid development of deep learning in recent years, automatic electroencephalography (EEG) emotion recognition has been widely concerned. At present, most deep learning methods do not normalize EEG data properly and do not fully extract the features of time and frequency domain, which will affect the accuracy of EEG emotion recognition. To solve these problems, we propose GTScepeion, a deep learning EEG emotion recognition model. In pre-processing, the EEG time slicing data including channels were pre-processed. In our model, global convolution kernels are used to extract overall semantic features, followed by three kinds of temporal convolution kernels representing different emotional periods, followed by two kinds of spatial convolution kernels highlighting brain hemispheric differences to extract spatial features, and finally emotions are dichotomy classified by the full connected layer. The experiments is based on the DEAP dataset, and our model can effectively normalize the data and fully extract features. For Arousal, ours is 8.76% higher than the current optimal emotion recognition model based on Inception. For Valence, the best accuracy of our model reaches 91.51%.

EEG emotion recognition model based on the LIBSVM classifier

Measurement ◽  
2020 ◽  
Vol 164 ◽  
pp. 108047 ◽  
Author(s):  
Tian Chen ◽  
Sihang Ju ◽  
Fuji Ren ◽  
Mingyan Fan ◽  
Yu Gu
Keyword(s):  
Emotion Recognition ◽  
Recognition Model ◽  
Model Based
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