scholarly journals Facial Expression Recognition Method Combined with Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ming Chen ◽  
Junqiang Cheng ◽  
Zhifeng Zhang ◽  
Yuhua Li ◽  
Yi Zhang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Facial Expression ◽  
Network Model ◽  
Facial Expression Recognition ◽  
Attention Mechanism ◽  
Expression Recognition ◽  
Recognition Method ◽  
Slow Speed ◽  
Running Speed

Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed. Firstly, group convolution is used to reduce network parameters. The channels of traditional convolution are grouped to cut off redundant connections so that the number of parameters decreases significantly. Secondly, the ERFNet network model was improved by combining the asymmetric residual module and the weak bottleneck module to improve the running speed and reduce the loss of accuracy. Finally, the attention mechanism was added into the feature extraction network to improve the recognition precision. The experiment shows that compared with traditional face recognition methods, the proposed method can improve the recognition precision and recall significantly; in CK+, Jaffe, and Fer2013 datasets, the recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.

scholarly journals A Facial Expression Recognition Method Using Improved Capsule Network Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yifeng Zhao ◽  
Deyun Chen
Keyword(s):  
Facial Expression ◽  
Network Model ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Regression Tree ◽  
Gaussian Process Regression ◽  
Learning Technology ◽  
Expression Recognition ◽  
Recognition Method ◽  
The Face

Aiming at the problem of facial expression recognition under unconstrained conditions, a facial expression recognition method based on an improved capsule network model is proposed. Firstly, the expression image is normalized by illumination based on the improved Weber face, and the key points of the face are detected by the Gaussian process regression tree. Then, the 3dmms model is introduced. The 3D face shape, which is consistent with the face in the image, is provided by iterative estimation so as to further improve the image quality of face pose standardization. In this paper, we consider that the convolution features used in facial expression recognition need to be trained from the beginning and add as many different samples as possible in the training process. Finally, this paper attempts to combine the traditional deep learning technology with capsule configuration, adds an attention layer after the primary capsule layer in the capsule network, and proposes an improved capsule structure model suitable for expression recognition. The experimental results on JAFFE and BU-3DFE datasets show that the recognition rate can reach 96.66% and 80.64%, respectively.

Lightweight facial expression recognition method based on attention mechanism and key region fusion

2021 ◽  
Vol 30 (06) ◽  
Author(s):  
Yinghui Kong ◽  
Zhaohan Ren ◽  
Ke Zhang ◽  
Shuaitong Zhang ◽  
Qiang Ni ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Attention Mechanism ◽  
Expression Recognition ◽  
Recognition Method

A Novel Facial Expression Recognition Method Based on AMSSD Model

2021 ◽  
Author(s):  
Qiuchen Wang ◽  
Xiaowei Xu ◽  
Ye Tao ◽  
Xiaodong Wang ◽  
Fangfang Chen ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Recognition Method

Facial Expression Recognition Method Based on Sparse Batch Normalization CNN

2018 ◽  
Author(s):  
Jun Cai ◽  
Ouan Chang ◽  
Xian-Lun Tang ◽  
Can Xue ◽  
Chang Wei
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Recognition Method ◽  
Batch Normalization

Computer Intelligent Nursing Care of Critically III Patients Based on Improved Patient Facial Expression Recognition Method

2014 ◽  
Vol 543-547 ◽  
pp. 2350-2353
Author(s):  
Xiao Yan Wan
Keyword(s):  
Facial Expression ◽  
Critically Ill ◽  
Nursing Care ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Appearance Model ◽  
Expression Recognition ◽  
Recognition Method ◽  
The Face

In order to extract the expression features of critically ill patients, and realize the computer intelligent nursing, an improved facial expression recognition method is proposed based on the of active appearance model, the support vector machine (SVM) for facial expression recognition is taken in research, and the face recognition model structure active appearance model is designed, and the attribute reduction algorithm of rough set affine transformation theory is introduced, and the invalid and redundant feature points are removed. The critically ill patient expressions are classified and recognized based on the support vector machine (SVM). The face image attitudes are adjusted, and the self-adaptive performance of facial expression recognition for the critical patient attitudes is improved. New method overcomes the effect of patient attitude to the recognition rate to a certain extent. The highest average recognition rate can be increased about 7%. The intelligent monitoring and nursing care of critically ill patients are realized with the computer vision effect. The nursing quality is enhanced, and it ensures the timely treatment of rescue.

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