# Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention
## 1 Introduction  
Facial expressions are one of the most important ways in which humans can effectively communicate their emotional states and intentions [1; 2]. The research on facial expression recognition has been enhanced, as more and more researchers focus their attention on the problem in the field of facial expression recognition. Automatic analysis techniques for facial expressions are being used in more and more fields, such as medical care, driver fatigue, robot interaction, and student classroom state analysis [3; 4; 5; 6; 7]. The changing needs have also given rise to many problems in different scenarios, and in the past period, the algorithms have been iterated continuously in FER-related problems [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18], and eventually, these algorithms have achieved good results. Meanwhile, in order to meet the requirements of different experiments, some datasets have been generated, such as CK+ [19], FER2013 [20], FERPLUS [21], etc. The construction of previous facial expression recognition systems is mainly divided into three steps: face detection, feature extraction, and expression recognition [22]. In dealing with facial expression recognition, the Dlib detector [23] is commonly used for face detection under various noise conditions. It can correct the images containing facesso that the images perform accurate face alignment. For face feature extraction, due to increasing computational power in recent years, researchers have designed many deep neural network algorithms to extract these geometric and appearance features [24; 25]. ResNet [26] is frequently seen in recent methods used for feature extraction. The feature maps obtained from the feature extractor are fed into the classifier used to match expression classes in facial expression recognition. Meanwhile, the great increase in arithmetic power has largely accelerated the development of deep learning [27; 28] and the research process of problems in the field of face recognition. Traditional facial expression recognition methods [29; 30] use manually extracted features such as the Histogram of oriented Gradients (HOG) [31], Local Binary Patterns (LBP) [32] and scale-invariant feature transformations (SIFT) [33] to deal with FER [34; 35; 36]. However, these methods usually have disadvantages, such as a long time spent, high cost, and poor portability. Compared with the traditional methods, deep learning methods can learn more deep features, such as rotation angle changes and changes caused by illumination. Additionally, due to the rapid development of the Internet in recent years, large-scale datasets can be obtained from challenging real-world scenarios in a relatively easier way [37; 38]. Deep learning methods benefit from these advantages and are more popular among researchers. Information on some datasets commonly used for facial expression recognition is shown in Table 1.  
Despite the great success of facial expression recognition, there are still some challenges, such as the noise generated by intra-class differences unrelated to expressions and the problem of overfitting due to insufficient training data caused by too small a dataset. Further, first, there are large intersubject differences in faces in the dataset due to age, gender, and race [22]. Intra-class differences become larger because of these uncertainties. The intra-class variance unrelated to expression recognition is shown in Figure 1. Second, experiments on deep neural networks require a large amount of training data. Insufficient training data can easily lead to overfitting of the model. Finally, directly using the global face as the input of the neural network will lose local detailed feature information [39]. [PERSON] [40] studied facial parts to find out the most important regions for human perception by masking the key parts, and the regions around the eyes and mouth are highly correlated with FER. From [PERSON] et al.