Facial Expression Recognition Using Random Forest Classifier

2020 ◽  
pp. 121-130
Author(s):  
Kamlesh Tiwari ◽  
Mayank Patel
Keyword(s):  
Random Forest ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Random Forest Classifier ◽  
Expression Recognition

Facial expression recognition from image sequences using twofold random forest classifier

2015 ◽  
Vol 168 ◽  
pp. 1173-1180 ◽  
Author(s):  
Xiaorong Pu ◽  
Ke Fan ◽  
Xiong Chen ◽  
Luping Ji ◽  
Zhihu Zhou
Keyword(s):  
Random Forest ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Image Sequences ◽  
Random Forest Classifier ◽  
Expression Recognition

Conditional convolution neural network enhanced random forest for facial expression recognition

2018 ◽  
Vol 84 ◽  
pp. 251-261 ◽  
Author(s):  
Yuanyuan Liu ◽  
Xiaohui Yuan ◽  
Xi Gong ◽  
Zhong Xie ◽  
Fang Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Convolution Neural Network ◽  
Expression Recognition

Tied gender condition for facial expression recognition with deep random forest

2020 ◽  
Vol 29 (02) ◽  
pp. 1 ◽  
Author(s):  
Liangji Zhong ◽  
Haibin Liao ◽  
Bin Xu ◽  
Shejie Lu ◽  
Jianfeng Wang
Keyword(s):  
Random Forest ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition

scholarly journals Facial Expression Recognition Based on Random Forest and Convolutional Neural Network

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 375 ◽  
Author(s):  
Yingying Wang ◽  
Yibin Li ◽  
Yong Song ◽  
Xuewen Rong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Great Success ◽  
Expression Recognition

As an important part of emotion research, facial expression recognition is a necessary requirement in human–machine interface. Generally, a face expression recognition system includes face detection, feature extraction, and feature classification. Although great success has been made by the traditional machine learning methods, most of them have complex computational problems and lack the ability to extract comprehensive and abstract features. Deep learning-based methods can realize a higher recognition rate for facial expressions, but a large number of training samples and tuning parameters are needed, and the hardware requirement is very high. For the above problems, this paper proposes a method combining features that extracted by the convolutional neural network (CNN) with the C4.5 classifier to recognize facial expressions, which not only can address the incompleteness of handcrafted features but also can avoid the high hardware configuration in the deep learning model. Considering some problems of overfitting and weak generalization ability of the single classifier, random forest is applied in this paper. Meanwhile, this paper makes some improvements for C4.5 classifier and the traditional random forest in the process of experiments. A large number of experiments have proved the effectiveness and feasibility of the proposed method.

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