### Research Motivations  
The problem addressed in this work is developing an efficient and accurate face recognition system to overcome challenges posed by face occlusion and improve recognition performance in real-world scenarios. Face occlusion, such as partial face coverage due to accessories, obstructions, or poor lighting conditions, is typical in video surveillance and urban security applications. Existing face recognition algorithms often struggle to handle these challenging conditions, leading to reduced accuracy and reliability due to wearing face masks. The motivations for developing a facial recognition system for people with and without face masks during the COVID-19 pandemic are as follows:  
1. The COVID-19 pandemic has underscored the importance of minimizing physical contact and maintaining hygiene. Implementing a contactless identification system like facial recognition can help reduce the risk of virus transmission through shared touchpoints, such as fingerprint scanners.
2. The widespread adoption of face masks as a preventive measure presents a challenge for traditional facial recognition systems designed for unmasked faces. Developing a system that can accurately recognize individuals both with and without face masks addresses this compliance monitoring need.
3. The pandemic has accelerated the adoption of face masks as a new societal norm. A facial recognition system capable of functioning effectively in the presence of face masks aligns with these evolving norms and ensures seamless integration into daily activities.
4. Surveillance and security applications benefit from accurate facial recognition, especially in crowded places like airports, public transportation, and essential service facilities. A system that can recognize faces despite masks contributes to enhanced public safety.
5. Traditional face recognition systems face accuracy and reliability issues when dealing with partial face coverage due to masks. This motivates the development of innovative solutions that can mitigate the negative impact of covers on recognition performance.
6. The pandemic has generated much data regarding masked and unmasked faces. Leveraging these datasets for research and development purposes offers a unique opportunity to create more robust and effective facial recognition systems.
7. Addressing the challenges posed by face masks in facial recognition requires innovation. Developing a system that can accurately recognize faces under diverse conditions reflects advancements in computer vision and deep learning techniques.  
The motivation stems from the need to adapt facial recognition technology to the current global context, ensuring safety, accuracy, and seamless integration with public health measures during and beyond the COVID-19 pandemic. The objective of this research is to propose a solution that leverages the ConvMixer architecture specifically designed for face recognition, along with the integration of an AdaBoost classifier, to handle face occlusion effectively, enhance feature representation, and achieve superior recognition accuracy compared with other state-of-the-art deep learning algorithms. The study aims to evaluate the proposed system's performance using benchmark datasets and validate its generalizability and efficiency for real-time deployment in face recognition applications. The goal is to provide a robust and practical face recognition solution that recognizes human faces even under challenging real-world conditions, contributing to advancing urban security and video surveillance technologies.