## 3 Materials and Methods
### System Implementation
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** \\ \hline Resnet-50 & 87.5\% & 84.6\% & 88.5\% & 87.5\% \\ Inception-V3 & 84.2\% & 83.3\% & 83.5\% & 81.5\% \\ DenseNet-161 & 85.5\% & 84.5\% & 89.3\% & 89.2\% \\ Ensemble-CNNs & 87.5\% & 83.6\% & 88.5\% & 87.1\% \\ AFR-Conv-Ada & 95.0\% & 97.0\% & 97.0\% & 98.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of different transfer learning algorithms compared with proposed AFR-Conv-Ada system when face occlusion is 35% on testing and training datasets. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** \\ \hline Resnet-50 & 83.5\% & 80.6\% & 84.5\% & 83.5\% \\ Inception-V3 & 80.2\% & 78.3\% & 78.5\% & 77.5\% \\ DenseNet-161 & 81.5\% & 80.5\% & 85.3\% & 85.2\% \\ Ensemble-CNNs & 83.5\% & 79.6\% & 83.5\% & 82.1\% \\ AFR-Conv-Ada & 94.3\% & 96.2\% & 96.5\% & 97.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of different transfer learning algorithms compared with proposed AFR-Conv-Ada when face occlusion is 45% on testing and training datasets. stable. This result supports our prior prediction that the loss of features due to occlusion could make it difficult for the masked model to learn. The models are trained for 40 epochs, and we do not make any drastic weight changes to our model layers. It should be noted that the VGG-16 model's layers are still frozen at this point, and it is only being used as a simplistic feature extractor. Our model has a validation accuracy of roughly 96%, which is a 6% improvement over our previous model, as seen in the preceding output. Overall, compared with our first basic CNN model, this model has a 24% higher validation accuracy. This demonstrates how effectively the proposed Conv-mixer model is implemented and improved in this paper. ### Computational Complexity Analysis

To calculate the Big O notation for the ConvMixer model and AdaBoost for recognizing human faces, we need to analyze the time complexity of each component involved in the algorithms. It should be noted that providing a precise Big O notation for the entire system might be complex without specific implementation details, but we can analyze the time complexity of key components. ConvMixer Model Time Complexity: Let's assume the ConvMixer model has L layers, each with C channels, a spatial resolution of H \(\times\) W, and a kernel size of \(K\times K\). Convolution Layer: The time complexity of a single convolution operation in a layer with a kernel size of K \(\times\) K and C channels is O(\(\mathbb{C}\times K^{2}\times H\times W\)). LayerNorm and ReLU Activation: The time complexity for LayerNorm and ReLU activation is typically negligible compared to the convolution operation. Since the ConvMixer model has L layers, the total time complexity for a single forward pass can be approximated as O(L \(\times\) C \(\times\) K\({}^{2}\times\) H \(\times\) W). AdaBoost Classifier Time Complexity: Let's assume the AdaBoost classifier has M weak learners, each with a time complexity of O(W) for a single prediction. Weak Learner Prediction: The time complexity of a single weak learner (e.g., a decision tree) for making a prediction is O(W). Since AdaBoost combines M weak learners, the total time complexity for making a single prediction using AdaBoost can be approximated as O(M \(\times\) W). Overall Time Complexity: The overall time complexity of the system, combining ConvMixer and AdaBoost, will depend on how these components are integrated and the number of iterations during training and inference. It could be represented as a combination of the ConvMixer model time complexity and the AdaBoost classifier time complexity

Figure 8: The vertical axis of the ROC (Receiver Operating Characteristic) curve is TPR (True Positive Rate) and the horizontal axis is FPR (False Positive Rate). AUC results obtained by AFR-Conv-Ada is 0.97.