## 3 Materials and Methods
### System Implementation
### Model Training

After the first, second, and fifth CONV layers, the network utilizes an overlapping max-pooling layer for training. Max-pool layers with strides smaller than the window size are referred to as overlapping max-pool layers. With a stride of 2, a 3 \(\times\) 3 max-pool layer is employed, resulting in overlapping receptive fields. The top-1 and top-5 mistakes were reduced by 0.4 percent and 0.3 percent, respectively, because of the overlapping. In detail, the focal loss function modifies the cross-entropy loss to concentrate learning on difficult negative cases. It is a dynamically scaled cross-entropy loss, meaning the scaling factor decreases as confidence in the proper class grows. This scaling factor, on the surface, appears to automatically down-weight the contribution of simple cases during training and quickly focus the model on challenging examples. Training: After the dataset has been prepared and the CNN has been selected, the network may begin to be trained. The values of the learnable parameters are altered at random during this method, and the related features are computed to offer a preliminary categorization of the pictures in the training set. The network's performance is measured using a metric (the loss function) that measures the similarity between the prediction and the ground truth. To improve the loss function and hence enhance correct predictions, parameters are iteratively modified. However, as previously mentioned, we must distinguish between two distinct scenarios. The first relates to a situation in which the whole network must be taught. In this situation, all of the network's parameters must be learned from scratch. In the second scenario, we can start with the pre-trained network and then modify some of its layers or add new layers to adapt it to the specific task you're interested in. This is particularly useful when you have a limited amount of data for the new task, as the pre-trained network has already learned useful features from a different, potentially larger data set. Fine-tuning allows you to leverage these learned features and adapt them to the new task, often resulting in faster convergence and improved performance. ### Results Analysis

In this experiment, data is gathered from a variety of popular datasets available on the Internet. Faces with masks appear in a small number of datasets. As a result, an augmentation approach is used on multiple common verification datasets to create the synthesized face mask evaluation dataset. The data augmentation technique is applied to LFW [31], CALFW [32], CPLFW [33], and CFP [34]. The LFW is a popular public face verification benchmark containing 13K photos and 5.7K IDs. To analyze the performance of the suggested AFR-Conv-Ada, 8500 face photos with masks were employed in total. First, we examine the training and validation accuracy of the model, as well as the loss function on the validation and training data. Using the 8500 images, Figure 7 illustrates the AFR-Conv-Ada model validation and training accuracy. As demonstrated in the figure, our model performs well in both validation and training. The 96.5% accuracy is achieved based on validation and training data, demonstrating that the AFR-Conv-Ada method performs efficiently on the selected dataset. Next, to gain further insight into the proposed detection algorithm's performance, the proposed AFR-Conv-Ada model's classification result is represented by a confusion matrix. The proposed technique was found to correctly categorize human faces despite occlusions. This indicates that all samples have been appropriately categorized according to the predicted value. As a result, it confirms that the AFR-Conv-Ada approach also has high detection accuracy on selected datasets. Since these are the most often used performance metrics, we utilize accuracy, recall, and F1-score to assess how well our model performs.