## 5 Discussion  
### Answers to Research Questions  
1.1 Research Question 1: How Does Preprocessing Data Affect Facial Recognition Systems, Specifically in the Context of Individuals Wearing Masks? Pre-processing data plays a crucial role in the performance of facial recognition systems, particularly when dealing with individuals wearing masks. Preprocessing techniques such as CutMix and MixUp can be used to augment the training data, resulting in improved accuracy. These techniques involve applying random cropping and mixing of images to the training dataset, which helps the model generalize better and reduces overfitting. Additionally, increasing image resolution and de-noising can be used to enhance the quality of input images, which can also improve the performance of the recognition system. 1.2 Research Question 2: How Can We Improve the Performance of Face Recognition Systems When Individuals Wear Masks? To improve the performance of face recognition systems when people wear masks, we need to identify an appropriate model and approach. One approach is to modify the structure and parameters of existing models, such as Google's FaceNet, which has a high rate of accuracy in face recognition. Additionally, we can adapt the dataset and image pre-processing techniques to better handle masked faces. For example, we can use a masked face dataset and train the model with it. Furthermore, we can make better use of data gathered from non-occlusion regions, such as the eyes and mouth, which are less likely to be obscured by a mask. Additionally, we can explore different types of masks and the level of occlusion they provide to improve the performance of the recognition system. 1.3 Research Question 3: How Does the Use of Both Masked Training and Test Images Affect Recognition Performance? The use of both masked training and test images can significantly affect recognition performance. When the faces of the subjects studied are obscured or masked, there are less data from which to learn, making accurate subject recognition more challenging. Furthermore, when both the training and test images are masked, recognition performance decreases. This is because the model has not seen enough unmasked faces during training and, therefore, struggles to recognize masked faces during testing. To overcome this, we can use a combination of masked and unmasked faces in the training dataset and also use data augmentation techniques to increase the diversity of the data. 1.4 Research Question 4: How Can We Improve the Performance of Face Recognition Systems When Faces Are Obscured or Masked? One potential approach to improving the performance of face recognition systems when faces are obscured or masked is to focus on preprocessing the data. This could involve techniques such as data augmentation, denoising, and resolution increase, which can help to improve the quality and diversity of the data used to train the model. Additionally, other techniques, such as adding CutMix and mixing augmentation, can also be used to improve the performance of the model. Another approach could be to change the parameters and \begin{table}
\begin{tabular}{l c c} \hline \hline
**Technique** & **Test Accuracy** & **Evaluation Time** \\ \hline Facenet model & 92.25\% & 36.2 s \\ - & Bottleneck & 83.26\% & 31.4 s \\ - & Block 8 (Inception Resnet C Block) & 81.14\% & 33.2 s \\ - & Block 17 (Inception Resnet C Block) & 75.73\% & 35.7 s \\ - & Mixed 7a (Reduction-B block) & 74.25\% & 34.6 s \\ - & Dropout and Bottleneck & 54.50\% & 33.5 s \\ - & Block 35 (Inception Resnet C Block) & 46.61\% & 35.1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance analysis of unmasked faces. structure of the model, such as adjusting the number of layers or the size of the filters, to improve its performance on masked data. 1.5 Research Question 5: How Can We Make Better Use of the Data Gathered from Non-Occlusion Regions?