# Facial Expression Recognition Based on Auxiliary Models
## 1 Introduction  
Facial expression recognition plays a great role in human-computer interaction. In the course of human communication, 55% of the information is conveyed by different facial expressions, voice constitutes 38% of a communicated message, and language only constitutes 7% [1], therefore facial expression recognition has attracted much attention in recent years [2; 3], and has many important applications in, e.g., remote education, safety, medicine, psychology and human-robot interaction systems. Although great progress has been made [4], it is difficult to acquire a facial expression recognition system with a satisfactory accuracy rate due to a variety of complex external conditions such as head pose, image resolution, deformations, and illumination variations. Hence, facial expression analysis is still a challenging work. Generally, facial expression recognition is composed of three steps: preprocessing, feature extraction and classification [5]. The image preprocessing plays two roles: firstly, the system access to the original image is not generally perfect in practical application, such as the effects of noise, illumination and contrast, hence it is necessary to enhance the image processing with a view to increasing the quality requirements. Secondly, the acquired image information does not meet the specific requirements of subsequent operations, such as size and angle, therefore it is necessary to perform the image processing. The image preprocessing serves as a transition, which needs to beconsidered comprehensively. Feature extraction is a key step in the whole recognition work [6], and the feature that we expect should minimize the distance of within-class variations of expression while maximizing the distance of between-class variations. If features are inadequate, even the best classifier would fail to achieve good performance. Among machine learning algorithms, features are extracted by hand, such as local binary patterns (LBP) [7], Gabor [8], local Gabor binary patterns (LGBP) [9], scale invariant feature transforms (SIFT) [10], and histograms of oriented gradient (HOG) [11]. Handcrafted features such as LBP, HOG, and SIFT have been widely used in the traditional approach owing to their proven performances under specific circumstances and their low computational cost in the feature extraction process. After feature extraction, the classification method should be applied to perform facial expression recognition, such as SVM [12], random forest [13], sparse coding [14], neural network [15], etc. Although these methods have achieved great success in specific fields, the handcrafted feature [16] has its inherent drawbacks. When we use handcrafted features, either unintended features that have no effects on classification may get included or important features that have a great influence on the classification may get omitted. This is because the features are \"crafted\" by human experts, and the experts may not be able to consider all possible cases and include them in the feature. Meanwhile, it is difficult to realize a good recognition result for big datasets with large inter-personal differences in facial expression appearance. To cope with the above disadvantages, deep learning methods [17; 18] are considered, especially the emergence of convolutional neural networks. Convolutional neural network (CNN) [19] is a very effective method to recognize facial emotions. They can perform the feature extraction and classification process simultaneously, and can automatically discover the multiple levels of representations in data. This is why they succeed in breaking the most world records in recognition tasks. The structure of early convolutional neural network was relatively simple. With the development of the relative research, the structure of convolutional neural network has been continuously optimized and its application field has been extended.