# Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention
## 2 Related Work  
**Deep learning and Attention in FER:** With the rapid development of computer vision in recent years, deep learning solutions are increasingly used to handle challenging tasks in FER compared to traditional manual feature extraction solutions. Meanwhile, traditional CNNs pay insufficient attention to important channel features and important regions of  
Figure 1: Intra-class differences are unrelated to expressions (the facial landmarks are detected by [44]). Images can have huge differences within the same expression class. For example, age and gender are different in different samples. As shown in the figure, the left and right images both represent the expressions of surprise and happiness, respectively, but they are visually very different. The above images are from AffectNet [7]. images, which restrict the accuracy of facial expression recognition. However, with the proposed attention, the model will give more attention and weight to important channels and regions, which is a good improvement to the deficiency of the original traditional model. Many existing models of deep learning and attention have achieved good performance. [PERSON] et al. [11] proposed a convolutional neural network with attention (ACNN) to automatically perceive blocked face regions and focus primary attention on unblocked expression regions for judgment. [PERSON] et al. [16] proposed a regional attention network (RAN) to recognize the occlusion of facial regions. [PERSON] et al. [15] proposed a self-cure network (SCN) to suppress bad data such as obscured face images in many large datasets, which prevents the network from overfitting incorrectly labeled samples. **Facial Landmark in FER:** The purpose of facial landmark detection is to identify the location of distinguishable key points on a human face. Currently, with the combination of facial landmark detection and deep learning techniques, such as face recognition [15; 20], facial expression recognition [13; 31], and face tracking [17], great progress has been made in the application of facial landmark detection, which has also facilitated the research of many accurate facial landmark detectors. Many accurate and widely used facial landmark detectors have been proposed, such as [4; 11; 14; 45]. Using these excellent detectors, researchers are able to better utilize facial landmarks as informative features in solving FER tasks. **Ensemble learning in FER:** Previous studies on ensemble learning have amply demonstrated that the combination of multiple networks outperforms individual networks [42]. When implementing network combinations, two key factors should be considered: (1) to ensure complementarity among multiple networks, we can increase the diversity of networks; and (2) choosing the right combination strategy can provide more effective assistance in aggregating committee networks [22]. For the second point, each member of the committee network can be combined at the feature level and at the decision level, respectively. For the aggregation of features, concatenating features obtained in different network paths is the most commonly used strategy [12; 46]. For the decision level, there are several most frequently used rules, such as simple averaging, majority voting, and weighted averaging. [PERSON] et al. [39] showed that people could effectively access the information displayed by facial expressions through two paths: local regions of the face and the whole face. Compared with previous methods, our proposed method is very effective in reducing the interference of irrelevant intra-class differences using a unique four key region segmentation method. Additionally, the method has good robustness due to the fixed region cropping approach. Comparing previous methods on the same dataset, the proposed method in this paper effectively expands the data volume via cropping segmentation to obtain detailed information in local paths while better meeting the experimental requirements of the dataset.