scholarly journals Weakly Supervised Local-Global Relation Network for Facial Expression Recognition

2020 ◽  
Author(s):  
Haifeng Zhang ◽  
Wen Su ◽  
Jun Yu ◽  
Zengfu Wang
Keyword(s):  
Facial Expression ◽  
Feature Fusion ◽  
Local Features ◽  
Relational Reasoning ◽  
Expression Recognition ◽  
Global Features ◽  
Global Relation ◽  
Weighted Fusion ◽  
Local And Global Features ◽  
Weakly Supervised

To extract crucial local features and enhance the complementary relation between local and global features, this paper proposes a Weakly Supervised Local-Global Relation Network (WS-LGRN), which uses the attention mechanism to deal with part location and feature fusion problems. Firstly, the Attention Map Generator quickly finds the local regions-of-interest under the supervision of image-level labels. Secondly, bilinear attention pooling is employed to generate and refine local features. Thirdly, Relational Reasoning Unit is designed to model the relation among all features before making classification. The weighted fusion mechanism in the Relational Reasoning Unit makes the model benefit from the complementary advantages between different features. In addition, contrastive losses are introduced for local and global features to increase the inter-class dispersion and intra-class compactness at different granularities. Experiments on lab-controlled and real-world facial expression dataset show that WS-LGRN achieves state-of-the-art performance, which demonstrates its superiority in FER.

scholarly journals Text Sentiment Classification Based on Feature Fusion

2020 ◽  
Vol 34 (4) ◽  
pp. 515-520
Author(s):  
Chen Zhang ◽  
Qingxu Li ◽  
Xue Cheng
Keyword(s):  
Hybrid Model ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Contextual Information ◽  
Sentiment Classification ◽  
Global Features ◽  
Softmax Classifier ◽  
Parallel Hybrid ◽  
Local And Global Features ◽  
Lstm Network

The convolutional neural network (CNN) and long short-term memory (LSTM) network are adept at extracting local and global features, respectively. Both can achieve excellent classification effects. However, the CNN performs poorly in extracting the global contextual information of the text, while LSTM often overlooks the features hidden between words. For text sentiment classification, this paper combines the CNN with bidirectional LSTM (BiLSTM) into a parallel hybrid model called CNN_BiLSTM. Firstly, the CNN was adopted to extract the local features of the text quickly. Next, the BiLSTM was employed to obtain the global text features containing contextual semantics. After that, the features extracted by the two neural networks (NNs) were fused, and processed by Softmax classifier for text sentiment classification. To verify its performance, the CNN_BiLSTM was compared with single NNs like CNN and LSTM, as well as other deep learning (DL) NNs through experiments. The experimental results show that the proposed parallel hybrid model outperformed the contrastive methods in F1-score and accuracy. Therefore, our model can solve text sentiment classification tasks effectively, and boast better practical value than other NNs.

An Enhanced Facial Expression Recognition Model Using Local Feature Fusion of Gabor Wavelets and Local Directionality Patterns

2020 ◽  
Vol 11 (1) ◽  
pp. 48-70 ◽  
Author(s):  
Sivaiah Bellamkonda ◽  
Gopalan N.P
Keyword(s):  
Facial Expression ◽  
Feature Fusion ◽  
Support Vector ◽  
Svm Classifier ◽  
Source Image ◽  
Expression Recognition ◽  
Fusion Model ◽  
Average Accuracy ◽  
Benchmark Datasets ◽  
Operator Fatigue

Facial expression analysis and recognition has gained popularity in the last few years for its challenging nature and broad area of applications like HCI, pain detection, operator fatigue detection, surveillance, etc. The key of real-time FER system is exploiting its variety of features extracted from the source image. In this article, three different features viz. local binary pattern, Gabor, and local directionality pattern were exploited to perform feature fusion and two classification algorithms viz. support vector machines and artificial neural networks were used to validate the proposed model on benchmark datasets. The classification accuracy has been improved in the proposed feature fusion of Gabor and LDP features with SVM classifier, recorded an average accuracy of 93.83% on JAFFE, 95.83% on CK and 96.50% on MMI. The recognition rates were compared with the existing studies in the literature and found that the proposed feature fusion model has improved the performance.

scholarly journals Facial Expression Recognition Based on Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiang Daihong ◽  
Hu yuanzheng ◽  
Dai Lei ◽  
Peng Jin
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Weighted Average ◽  
Poor Performance ◽  
Attention Mechanism ◽  
The Self ◽  
Expression Recognition ◽  
Residual Network ◽  
Global Features ◽  
Low Efficiency

At present, traditional facial expression recognition methods of convolutional neural networks are based on local ideas for feature expression, which results in the model’s low efficiency in capturing the dependence between long-range pixels, leading to poor performance for facial expression recognition. In order to solve the above problems, this paper combines a self-attention mechanism with a residual network and proposes a new facial expression recognition model based on the global operation idea. This paper first introduces the self-attention mechanism on the basis of the residual network and finds the relative importance of a location by calculating the weighted average of all location pixels, then introduces channel attention to learn different features on the channel domain, and generates channel attention to focus on the interactive features in different channels so that the robustness can be improved; finally, it merges the self-attention mechanism and the channel attention mechanism to increase the model’s ability to extract globally important features. The accuracy of this paper on the CK+ and FER2013 datasets is 97.89% and 74.15%, respectively, which fully confirmed the effectiveness and superiority of the model in extracting global features.

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