## 3 Materials and Methods
### System Implementation
Overall, addressing these challenges will lead to a robust and comprehensive approach for face recognition capable of handling occlusions and delivering accurate results across diverse face images. The fundamental purpose of this research is to present a new DL model for identifying individuals with face occlusion and face masks. The proposed system efficiently addressesthis complicated challenge. Furthermore, compared to state-of-the-art classification approaches, greater classification accuracy is achieved. We talked about the advantages of the proposed AFR-Conv-Ada approach for recognizing humans despite facial occlusion. During the COVID-19 era, several issues prompted us to utilize CNN as a foundation model based on Conv-mixer with AdaBoost to recognize occluded human faces. The following are the factors considered: (1) Motivated by the AFR-Conv-Ada model's outstanding performance in other research disciplines (2) The architecture of the previous AFR-based technique has a high time complexity. (3) To properly assess the existing model's decreased performance. (4) Face recognition detection accuracy is lacking. In the proposed work, different datasets are used, such as LFW [21], CALFW [22], CPLFW [23], and CFP [24]. First, the data size is increased using augmentation. The first flow of the depthwise separable convolutional is then utilized to extract features from human face images using CNN blocks and residual connections. Finally, those features are used in face recognition by providing the feature map to an AdaBoost classifier. The current CNN architecture has a lot of computations involved and required parameters; hence, it requires a lot of hardware acceleration. In computer vision-related tasks, a Conv-mixer model has already been successfully applied to feature extraction. A visual example of negative images containing face occlusion predicted wrong detection result is shown in Figure 9.

In this work, the proposed technique is compared to other state-of-the-art models in terms of computing complexity. The proposed work took 163 s to process in total. The AlexNet and VGG-16 took 209s and 184s, respectively, to process. Based on the results, it was concluded that the suggested model took less time to identify human beings. This shows that the proposed model is more efficient than its rivals. The GPU is also used by Google Colab to test the computational performance of the proposed AFR-Conv-Ada system on this dataset. A GPU can be thought of as a set of cores with a software layer that enables parallel processing. In contrast to the CPU, the GPU's performance in terms of execution time and computing speed is impressive. Table 6 is used to measure the performance of different transfer learning algorithms compared with the proposed AFR-Conv-Ada classifier. Table 8 shows high performance in verification by using AFR-Conv-Ada compared to other techniques. In this table, methods are trained on a dataset with 45% face occlusion on testing and training datasets. The proposed technique's verification performance is only marginally improved by training on the synthetic dataset. In fact, after training with the synthesized dataset, the verification performance on the cross-age CALFW dataset dropped. In all synthesized datasets on the table, however, the approach provides significantly improved verification performance. The results of different transfer learning algorithms compared to the proposed AFR-Conv-Ada system for face occlusion are displayed in Table 8. The AFR-Conv-Ada approach improves verification performance by about the same amount as shown in this table. In addition, the ROC curve is also used to measure

Figure 9: A visual example of negative images containing face occlusion leading to incorrect detection result. the performance of the proposed classifier AFR-Conv-Ada in training and test datasets by 10-fold cross-validation. Figure 8 shows the ROC curve of the proposed method. The widespread adoption of face masks as a COVID-19 pandemic prevention tool is the driving force behind this endeavor.