## 3 Materials and Methods
### System Implementation
Conducting studies with larger and more diverse datasets, including variations in pose, illumination, and expressions, would validate the algorithm's robustness across different real-world conditions. Moreover, incorporating advanced evaluation metrics and benchmarking against other leading algorithms would facilitate a more comprehensive performance analysis. Exploring transfer learning techniques by pre-training the ConvMixer on larger-scale face-related datasets could potentially enhance recognition accuracy. Additionally, investigating hybrid architectures that combine ConvMixer with other deep learning models may open new avenues for achieving even higher performance levels. Finally, considering the ethical implications and privacy concerns related to face recognition technologies is essential in future works, ensuring responsible and transparent use of the proposed algorithm in real-world applications. By addressing these limitations and pursuing future research in these directions, the proposed ConvMixer and AdaBoost approaches can be further strengthened and contribute to advancements in the field of face recognition. ## 6 Conclusions

The COVID-19 outbreak has led to people wearing masks when they go out, yet existing face recognition systems (FRS) are unable to detect masks. Conv-Mixer-based techniques and AdaBoost classifiers are proposed as improved approaches in this research, which uses deep learning algorithms to tackle the above challenges. This study compared the face verification performance of human specialists to state-of-the-art artificial face recognition methods in a comprehensive joint evaluation and in-depth analysis. The fundamental purpose of this research is to present a new DL model for identifying faces with face occlusion and face masks effectively. This proposed system addresses the intricacy problem, limits the database, and obtains the informative features efficiently in the present DL design. Additionally, compared with other classification schemes, greater classification accuracy is attained. The proposed AFR-Conv-Ada approach recognizes faces by ignoring face occlusion. During the COVID-19 era, several issues prompted us to utilize CNN as a foundation method based on Conv-mixer with AdaBoost to recognize occluded human faces. The factors are as follows: (1) the AFR-Conv-Ada model's outstanding performance in other research disciplines; (2) the complexity issue of the architecture of the previous AFR-based technique; (3) the need to properly assess the existing model's decreased performance; and (4) inadequate face recognition detection accuracy. In this paper, face recognition using the convolutional mixer (AFR-Conv) algorithm is developed to handle face occlusion problems. A novel AFR-Conv architecture is developed by assigning priority-based weights to the different face patches along with residual connections and an AdaBoost classifier for the automatic recognition of human faces. To begin, we use the data augmentation method to enhance the size of face images datasets. Afterward, this AFR-Conv algorithm is executed to obtain robust characteristics from images. Finally, an AdaBoost classifier is employed to recognize the identity of people. For the training and evaluation of the AFR-Conv model, a set of face images is collected from online data sources. The experimental results of the AFR-Conv approach are presented in terms of precision (PR), recall (RE), and detection accuracy (DA) metrics. Specifically, it achieves 94% PR, 91% RE, and 90% DA on 8500 face images. These experimental results demonstrate that this proposed methodology outperforms other algorithms for the classification of faces. Hence, the proposed AFR-Conv significantly improves performance compared with other existing systems. **Author Contributions:** Conceptualization, Q.A., T.S.A., G.P. and M.E.C. ; Data curation, G.P. ; Funding acquisition, T.S.A. ; Investigation, T.S.A. and G.P. ; Methodology, Q.A., T.S.A. and M.E.C. ; Project administration, Q.A., G.P. and M.E.C. ; Resources, Q.A., T.S.A. and M.E.C.