# Facial Expression Recognition Based on Auxiliary Models
## 1 Introduction
In recent years, the research on the structure of convolutional neural network is still very hot, and some network structures with excellent performance have been proposed. The research results of convolutional neural network in various fields make it one of the most concerned research hotspots. In the 1980s and 1990s, some researchers published relevant research work of CNN, and achieved good recognition results in several pattern recognition fields. [PERSON] et al. proposed a CNN model called LeNet-5, and the success of handwriting character recognition about this model has aroused the attention of academia on convolutional neural network. However, CNN is only suitable for small image recognition at this time. For large-scale data, the recognition effect is not good. At the same time, Convolutional neural network is gradually developing in many fields such as speech recognition, object detection, face recognition etc. In 2012, [PERSON] et al. used the extended CNN model (AlexNet) to win the champion in ImageNet Large Scale Visual Recognition Challenge (LSVRC) with a huge advantage of accuracy over the second place of \(11\%\), making convolutional neural network become the focus of academia. Since AlexNet, many new convolution models have been proposed: such as the VGG (Visual Geometry Group) proposed by Oxford University, Google's GoogleNet, Microsoft's ResNet, etc., and these models have been constantly breaking AlexNet's ImageNet record. Since Convolutional Neural Network (CNN) has already proved its excellence in many image recognition tasks, we expect that it can show better results than already existing methods in facial expression prediction problems. Most CNN -based facial recognition tasks use the entire face image as the input information, but what we found in our observations is that the judgment of facial expression is usually completed based on the information of several sensitive components in some areas of the face, such as eye, nose, and mouth. Other areas of the face contribute very little to the main feature of expression. If we use the entire face image to extract the features, the extracted feature vectors might lose some important information because it failed to catch the focus of the facial features. If the above feature information is adopted in the test experiment, it will make the test results very irrational, because the extracted feature information by using the above methods is greatly different from the real feature information. This is often caused by two factors: (1) There are too few data. Unlike large scale visual object recognition databases such as ImageNet [17], most existing facial expression recognition databases do not have sufficient training data, which leads to the overfitting problem. (2) The learning mechanism of the single-task CNN network itself has some limitations, and problems cannot be solved by the only one CNN model. Aiming at the above problems, some improvements have been proposed in this paper. To solve the problems caused by too few data, this paper divides some organ images that have important contributions to facial expression recognition from the raw images, which can not only improve the quantity of datasets, but also improve the quality of extracted information. Meanwhile, since it is difficult for the CNN model based on single task to improve the overall accuracy rate in the recognition task, this paper proposes a multi-task learning-based recognition model, which can modify the expression features extracted from the raw images with the help of the auxiliary model, so that the final extracted feature information is more in line with the ideal expression feature information. The paper is arranged as follows: After this introduction, Related Work (Section 2) is presented which focuses on the various approaches with better performance that scientists have taken recently. Section 3 focuses on the main components of the architecture proposed in this paper. Section 4 presents the experiments and its results. Finally, Section 5 summarizes and concludes this paper.