# Deep Learning Mask Face Recognition with Annealing Mechanism
## 1 Introduction
[14] proposed a model called LLE-CNN for masked face detection. The model is divided into three parts. The first part combines two pre-trained models for the high-dimensional feature description of the masked face in the image. The second part uses the locally linear embedding (LLE) algorithm to perform a similarity transformation with the synthesized normal face, in order to repair the obscured or damaged areas of the face. The third part is the validation module, which improves the face area of the retouched candidates and performs classification and regression validation. [PERSON] et al. [15] proposed a multimodal corpus called Biometric Russian Audio-Visual Extended MASKS (BRAVE-MASK). It is used to analyze the facial features and voice of the face of the person wearing the mask. [PERSON] et al. [16] proposed a CNN model called MaskNet that automatically assigns higher weights to unmasked face features. Occluded parts are given lower weights, and this is validated against a dataset of real-life faces and a synthetic occlusion face. [PERSON] et al. [17] proposed a deep learning model called Face Recognition with Occlusion Masks (FROM) that uses a large number of occluded face images to learn the features of the occluded area and dynamically clean these features. [PERSON] et al. [18] used a multitude of existing face recognition models for MFR and performed analytical tests. It is proposed that the model performance can be improved by adding a masked face image during the training process, and that the parameters of face recognition are different from those of mask face recognition and need to be reset. The above research on MFR is full of novel algorithms incorporating deep learning, and all of them have good performance. However, as the study [19; 20] reminds us, the computational cost of deep learning is extremely high, and high economic and environmental costs are required to reduce the error rate. From the research of [PERSON] et al. [21], it is concluded that the factors that affect the performance the most are hyperparameters and random restarts weight, and the network architecture does not have much influence. Therefore, if the random restarts weight and hyperparameters are set properly, different models of the architecture yield a similar performance. To summarize the above, in order to simplify the complex computation, this study uses the CNN framework validated by the imagenet [22] competition and FaceNet [23] as the training method to complete the MFR. The reason for using FaceNet is that its main network architecture is not fixed, and can be combined with transfer learning to use different CNN architectures, which is more flexible. The FaceNet training method mainly uses the triplet loss function to allow the network weights to be converged and not to retrain the model in the future if new samples are added for classification. The most important concept of transfer learning is to apply a problem solvable model to different but related problem domains [24], which also saves training time and solves the problem of random restarts weighting. As for the hyperparameter adjustment, this study tries to start from the learning rate (LR) which is not easy to adjust. The neural network model selects different optimizers for gradient descent during the learning procedure, intending to find the best solution in the global domain. Choosing the right optimizer allows the model to converge and find the best weights easily, and whichever optimizer is chosen requires LR to allow the model to update the parameter weights. In general, LR is a fixed value, but the disadvantage is that if it is set too large, the model does not converge easily. If the setting is too small, the model will converge too slowly. Improperly set values tend to make the model find only the local optimal solution, rather than the ideal global optimal solution. In this study, we will use the cosine annealing (CA) mechanism proposed by [PERSON] et al. [25] that can dynamically adjust the LR of the optimizer.