## 5 Discussion
### Answers to Research Questions
One potential approach to making better use of the data gathered from non-occlusion regions is to focus on feature extraction and selection techniques. This could involve techniques such as principal component analysis (PCA) or linear discriminant analysis (LDA) to identify the most informative features in the data. Additionally, techniques such as deep learning can also be used to extract features from non-occlusion regions of the face and use them to improve the performance of the model. Another approach could be to use data from both occluded and non-occluded regions to train the model, which can help to improve its generalization performance. 1.6 Research Question 6: How Can We Evaluate the Performance of Face Recognition Systems When Faces Are Obscured or Masked? One potential approach to evaluating the performance of face recognition systems when faces are obscured or masked is to use a dataset of masked faces. This dataset could be used to train and test the model, and its performance could be evaluated using metrics such as accuracy, precision, recall, and F1-score. Additionally, it can be evaluated using ROC curve analysis. Another approach could be to use a dataset of both occluded and non-occluded faces, which can help to provide a more comprehensive evaluation of the model's performance. Additionally, real-world evaluation can be carried out by using the models on a real-time scenario, so as to check its accuracy and efficiency in identifying people with masks. 1.7 Research Question 7: How Can We Improve Recognition Performance When Both the Training and Test Images Are Masked? One way to improve recognition performance when both the training and test images are masked is to use data augmentation techniques, such as CutMix and MixUp, on the training dataset. This can help increase the diversity of the data and make the model more robust to variations in mask types and levels of occlusion. Additionally, using a deep CNN, such as FaceNet, to convert the input face image into a vector and calculate the Euclidean distance between the two vectors with the vectors of each face in the dataset. This can help the model focus on the non-occluded regions of the face, such as the eyes and mouth, and improve recognition performance. Additionally, data preprocessing techniques such as resolution increase and denoising can help make the non-occluded regions more distinguishable. Finally, using a large, diverse dataset that includes a variety of mask types and levels of occlusion can also help the model better utilize the non-occluded regions. 1.9 Research Question 9: How Can We Improve the Performance of Facial Recognition Systems When Faces Are Obscured by Masks? One approach to improve the performance of facial recognition systems when faces are obscured by masks is to use data preprocessing techniques such as 'CutMix' and 'MixUp', which have been found to improve the accuracy of the FaceNet model. Another approach is to make changes to the model's parameters and structure, such as increasing the resolution and denoising the input image, in order to better capture the features of the face that are visible despite the mask. Additionally, it may be beneficial to use a different dataset that includes more examples of masked faces to train the model to better recognize obscured faces. 1.10 Research Question 10: What Are Some Potential Uses of Improved Facial Recognition Systems for Masked Faces? Improved facial recognition systems for masked faces have the potential to be used in a variety of contexts, including law enforcement, security, and surveillance. For example, it could aid law enforcement in identifying criminals hiding behind masks and to detect individuals who are trying to evade detection by wearing masks. It could also be used in security settings, such as airports and train stations, to identify people trying to enter a restricted area while wearing a mask.