# HSAW: A Half-Face Self-Attention Weighted Approach for Facial Expression Recognition
## 2 Related Works  
Traditional facial feature extraction algorithms can be separated into two categories: (1) geometric-based methods, such as Active Appearance Models (AAM) [5], and (2) appearance-based methods, such as LBP [6] and Gabor Wavelet Representation. After the feature description, the features are fed into a classifier, such as SVM [7] and K-nearest Neighbors (KNN) [8], for recognizing different facial expressions. Therefore, the classifier's performance depends mainly on the quality of the extracted features. In [9], various feature extraction techniques combined with different classification algorithms were presented to find the best combination that can be used for emotion intensity recognition. The results with LBP features are better than those using HOG and Gabor features. AAM tracks faces and extracts facial features, and then uses support vector machines to classify facial expressions. In the dataset CK+ [10], the accuracy of this architecture has reached over 65%, with the best recognition accuracy for happy emotions being 100%.  
Deep neural networks have become popular in recognizing facial expressions and other computer vision tasks in recent years. VGG Net [11] used very small convolution filters (3 \(\times\) 3) to increase the architecture depth, where the small-size filter can make the decision function more discriminative and decrease the number of parameters. The network often stacked several convolutional layers and then followed one pooling layer. The architecture can significantly improve the prior-art configurations when there are 16 or 19 weight layers in the network. GoogLeNet [12] is a 22-layer deep network. The width and depth of the network are increased compared with previous networks. The main structure of the network is the \"Inception\" layers, which contain several parallel convolution branches. \"Inception\" layers have different sizes of convolution filters, and the input images can convolve to varying scales of feature maps. In ResNet [13], skip connections are added between the network's input and output layers. This structure not only increases the training speed and improves the training effect of the model, but also avoids gradient disappearance and network degradation.  
Most works were inspired by the above-mentioned deep network architectures for the facial expression recognition task. [PERSON] et al. [14] proposed a facial expression recognition network with visual attention. In this network, the deep convolution features are extracted from the face and, thus, the regions of interest are detected and used to classify expressions. In [15], a GAN-based face frontalization method was presented. The generator formalizes the input face images, and the identity and expression characteristics are preserved at the same time. Then, the discriminator distinguishes between the real images and the generated face images. [PERSON] et al. [3] further considered local facial regions and used trained multi-channel convolutional neural networks to extract local features for eyebrows, eyes, and mouth, thereby improving facial expression recognition accuracy.  
However, the current mainstream methods for facial expression recognition are based on the entire face. On the one hand, this method ignores the asymmetry of facial expressions. On the other hand, half-face-based recognition methods can reduce the number of model parameters, making recognition more efficient. Therefore, this paper first verifies the asymmetry of facial expressions from statistical and computer vision perspectives and then proposes an expression recognition algorithm based on a half-face weighted self-attention mechanism.