# Deep Learning Mask Face Recognition with Annealing Mechanism
## 1 Introduction  
To prevent the spread of the virus effectively, the World Health Organization (WHO) recommends that people wear masks and maintain social distance when going out since COVID-19 began to ravage the world in 2020. However, wearing a mask will cover most of the face. This increases the security risks in places that originally required people to reveal their full face (such as banks, shops, etc.). The face information that can be used for face recognition includes eyes, nose, mouth, and profile, etc. This technology has matured thanks to deep learning. However, the effect of wearing a mask means less of the face can be identified, making the existing face recognition system another challenge. According to a 2020 National Institute of Standards and Technology (NIST) [1] test of numerous face recognition algorithms, wearing a mask can reduce the performance of an otherwise stable face recognition system because less information about the face can be recognized. For example, leading biometric research institutes such as NEC [2] and Thales [3] were also forced to re-examine and adjust their algorithms to improve the accuracy of face recognition systems wearing masks after the COVID-19 pandemic. This highlights the shortcomings of the current mask face recognition (MFR) algorithm. After the outbreak of COVID-19, many related retrospective studies of MFR by [PERSON] et al. [4] until 2021 show that MFR has become a hot field. [PERSON] et al. [5] proposed a MFR algorithm based on a large margin cosine loss called MFCosface. The sample features are mapped into a feature space, allowing a larger distance between classes. An Att-inception module that combines the Inception-Resnet module and the convolutional block attention module is added to enhance the feature weights of unobscured regions. [PERSON] et al. [6], inspired by amodal perception [7], used a module composedof two different neural networks for MFR. The first module is de-occlusion using GAN for face restoration. The second module is distillation, which uses a pre-trained face recognition model for transfer learning to learn face features from the pre-trained model. The two modules are combined for MFR. [PERSON] et al. [8] proposed a mask learning strategy to find and discard damaged (obscured) features for Face Recognition. Firstly, they use Pairwise Differential Siamese Network (PDSN) to train the difference between masked and unmasked face features and build a mask dictionary. The correspondence between the masked face area and the corrupted feature elements in each item of the dictionary is called the Feature Discarding Mask (FDM). The corrupted features are removed for face recognition by combining the relevant dictionary to generate the FDM and the original feature calculation. [PERSON] et al. [9] proposed a face recognition model called Embedding Unmasking Model (EUM) based on the convolutional neural network (CNN) model and a novel Self-restrained Triplet Loss to evaluate the results of recognition by EUM. [PERSON] [10] combined deep learning with traditional image processing to solve the MFR. First, the features of the unobscured areas of the eyes and forehead were obtained using a trained CNN model, and then a bag-of-features paradigm was used in the last layer of the network to quantify the features for classification. [PERSON] et al. [11] first obtained the unmasked eyes and forehead areas in the face image and used ESRGAN [12] to perform an image super-resolution. Next, the steps are divided into two parts based on the neural network. One is to use the YCbCr color space in the image for the frequency domain broadening followed by Fast Independent Component Analysis feature reduction to obtain the features. The second is to use image RGB and enhance MBConvBlock in EfficientNet [13], to obtain the spatial fine-grained feature using the semantic grouping layer and group bilinear layer. Finally, the two features are combined and connected to the multi-layer perceptron (MLP) output. [PERSON] et al.