# Deep Learning Mask Face Recognition with Annealing Mechanism
## 2 Dataset  
The dataset on mask faces. MaskedFace-Net was proposed by [PERSON] et al. [32] The images in the dataset were created by Flickr-Faces-HQ (FFHQ) [33] as a composite of faces wearing masks, and faces not wearing masks properly. [PERSON] et al. [34] proposed the TFM dataset. The dataset collection showed the masked faces in the wild wearing a variety of masks and at different angles. Other masked face in the wild datasets include the MAsked FAces dataset (MAFA) proposed by [PERSON] et al. [14] and the Real-World Masked Face Dataset (RMFD) proposed by [PERSON] et al. [35]. However, the above study was mainly used to detect whether people were wearing masks or wearing them correctly. The quality of each dataset varies and requires a lot of time for data cleaning. This study focuses on MFR, so the above dataset was not selected. This study uses VGGFace2_HQ_CROP [36], which is a Dataset modified from VGGFace2 [37]. VGGFace2 is a large collection of 3.31 million images, divided into 9131 subjects, each representing a different face. The average pixel size of each image in the VGGFace2 dataset is \(137\times 180\) pixel [38]. Since the size and quality of each image in the VGGFace2 dataset varies, some scholars have high-quality the VGGFace2 dataset and named it VGGFace2_HQ [39]. In VGGFace2_HQ, each image is face alignment, and the size is standardized to 512 \(\times\) 512. VGGFace2_HQ_CROP is the VGGFace2_HQ face retrieval version. There are three main reasons for using VGGFace2_HQ_CROP dataset: 1. the number of images is large enough, 2. the image quality is stable, and 3. the images have already completed face acquisition, so there is no need to perform related operations during the experiment. Table 1 shows the relevant data for the three datasets. Although the total number and subjects of VGGFace2_HQ_CROP images were significantly reduced, they were still sufficient to allow this study to proceed.  
Figure 1 is an example of the corresponding image of the dataset. Figure 1a is the original VGGFace2 image, Figure 1b is VGGFace2_HQ, and Figure 1c is VGGFace2_HQ_CROP. Both VGGFace2_HQ and VGGFace2_HQ_CROP have already performed pre-processing such as face alignment and cropping on the original VGGFace2 to make the image quality of the whole dataset more stable.  
In this study, the top 600 classifications from VGGFace2_HQ_CROP were selected. One subject represented one person, 500 subjects were the training set, and 100 subjects were the test set. MaskTheFace [40] was used to synthesize all the images of 600 different subjects of masks and was named VGGFace2_HQ_CROP_MASK_600 (MASK600), and the final data obtained is shown in Table 2. In Table 2, the Train set is 500 subjects, and the Test set is 100 subjects, totaling 600 subjects. The number of images is the total number of original and synthetic images. Take the Train set as an example. The number of original images is 46,758, and each original image corresponds to one synthetic image, so the total number of images is 93,516.  
Figure 2 is an example of MASK600 correspondence image, Figure 2a above is the original image, and Figure 2b below is the synthesized image.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Subjects** & **Numbers** & **Image Size** \\ \hline VGGFace2 & 9131 & 3.311 M & 137 \(\times\) 180 (average) \\ VGGFace2\_HQ & 8631 & 1.160 M & 512 \(\times\) 512 \\ VGGFace2\_HQ_CROP & 4605 & 0.624 M & 160 \(\times\) 160 \\ \hline \hline \end{tabular}
\end{table}
Table 1: VGGFace2 Dataset corresponding table.  
Figure 1: Example of dataset correspondence. (**a**) VGGFace2 [37]; (**b**) VGGFace_2_HQ [39]; (**c**) VGGFace2_HQ_CROP [36].