# Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene
## 4 Experiments  
The effective training of the deep face recognition model is based on a sufficiently large training dataset. We choose the CASIA-Webface [18] dataset as the training set. After cleaning, the amount of data is approximately 1 M with more than 10,000 categories. We firstly perform accurate face detection through the Multitask Cascaded Convolutional Networks(MTCNN) [19] model and obtain the coordinates of the five key points corresponding to the face image. The affine transformation between the key points is used to align the faces to obtain the inputs of the CNN. Then we normalize each image size to \(112\times 112\). At the same time, we use the estimation of the coordinates of the five points to obtain the pose results. Then, we obtain the pose categories through the pose classification function \(N(p,y,r)\).  
After using the PnP method mentioned in Section 3.1 to solve the initial solution, a deep CNN, which adopts the Visual Geometry Group(VGG) [20] network architecture, is used to learn a direct mapping from the picture to the three pose angles. The pose estimation network is trained separately within the images and the initial solution calculated by the PnP. This network does not participate in iterations during trunk training.  
The backbone model we adopt is based on LResnet50E-IR [3,4] in ArcFace, which is used to find the segmentation hyperplane of the feature space. However, it is no longer based on the segmentation of the Euclidean feature distance space. Instead, it is only mapped to the cosine angle space to find the appropriate segmentation hyperplane. The model can achieve efficient and robust segmentation in the angle space, while the angle space-based metrics closely matches with the cosine similarity-based verification.  
The overall network structure is shown in Figure 6.  
The network starts with an input layer. Then, it gradually extracts features from coarse to fine through four blocks, and the size of the output feature graph of each block is halved. Block1 contains 3 Squeeze-and-Excitation residual (SE-Res) blocks and the output feature channel is 64. Block2 contains 4 SE-Res blocks and the output feature channel is 256. Block4 contains 3 SE-Res blocks and the output feature channel is 512. If the input image size is \(112\times 112\), the feature sizes of the blocks are \(56\times 56\), \(28\times 28\), \(14\times 14\), and \(7\times 7\), respectively. Finally, a dropout layer is used to prevent overfitting, and the 512-dimensional feature representation is output through the full connection layer. After each full convolutional layer, the BN layer is used to accelerate network convergence and the PReLU function is used to enhance nonlinear mapping capability. The structure of SE-Res [21] is shown in Figure 7.  
Figure 6: The overall network architecture.  
Based on the Res structure, SE-Res uses the dependency relationship between feature channels to learn the weight coefficient of each feature map to represent the important relationship of the feature map. Then, the feature map of the Res block is multiplied channel by channel to obtain the feature map representation containing a lot of information. SE-Res can suppress unimportant features and amplify important features.  
In this study, we chose CASIA-Webface dataset as the training set, while the LFW, CFP, and IJB-A dataset are only used for evaluating algorithm performance, they do not actually participate in the updating of the network parameter \(W\).  
We adopt joint training and trained pose loss and softmax loss on the same dataset at the same time. Then, we set the weight of the pose loss to 0.005. The initial learning rate is 0.005 and the batch size is 32.