# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 1 Introduction
Facial expression recognition technology has also been further developed in terms of flexibility, artificial dependence, and natural environment. With the development and deepening of facial expression research, expression recognition methods are becoming more and more diverse. For feature extraction and expression recognition research in uncontrolled environments, scholars have proposed many superior network frameworks. [PERSON] et al. [14] proposed a multimodal emotion recognition framework based on different possible fusion methods. [PERSON] et al. [15] studied the expression recognition network in the case of partial face occlusion: In this approach, the image Gabor wavelet features are extracted first, and then the Deep Belief Network (DBN) is used for training and recognition. Expression images are pre-processed with partial occlusion (the occlusion range does not exceed 50%). At the same time, the Gabor wavelet is used to extract features in multiple directions and scales to increase the number of training samples and prevent the network from overfitting. [PERSON] et al. [16] considered that different faces and different parts of the face express different degrees of expression, and used some active expression regions to train a multi-channel deep belief network: First, the face is detected through the sliding window, and then the facial features are detected in turn; then the HOG features of each small area [17] are calculated using the deep belief network for deep learningand the trained features are used as detection components for the Gabor feature fusion. Finally, the network is trained by sparse self-encoding, and the recognition result is obtained. In this way, the redundancy between the expression information can be removed without prior alignment or other preprocessing of the face image. On the facial expression recognition data set, it has obtained a superior recognition effect. [PERSON] et al. [18] designed a multimodal emotion recognition technique that automatically learns the weighted sum of decision parameters for each modality. [PERSON] [19] and others believe that there is a large amount of redundant information in the facial expression sequence. By calculating the change of the key information points of the face during the expression change process, the face image with the peak expression was selected to determine the peak expression in the face position to improve the accuracy of facial expression recognition. To solve the problem of facial expression feature recognition with uncertainty, [PERSON] et al. [20] proposed a self-cured network (SCN) where the attention mechanism is added to the classification branch in the network structure, and the idea of regularization sorting is added to reduce the importance of uncertain samples. Finally, in order to further improve the network mechanism, a re-labeling module is added to modify the uncertain sample data. [PERSON] et al. [21] proposed an efficient self-cured network (ESCN) model. A feature weighting network is used to receive facial expression picture video frames and generate class recognition capabilities. then, a multi-scale attention mechanism feature fusion network is used to capture and weight facial expression regions, and finally linear aggregation classification is used for re-label correction. In summary, the current deep facial expression recognition system has the following problems:  
1. Issues such as illumination, occlusion, and large face poses cause certain uncertainty in expression labels under uncontrolled conditions, which affects the accuracy of expression recognition;
2. The database of reliable facial expression recognition is small in scale and poor in quality, which affects the recognition performance;
3. Deep learning involves many hyperparameters, building an expression recognition model takes a long time to train, and the model is large.