## 3 Materials and Methods
### System Implementation
The relationships between human expert verification behaviors and automatic face recognition solutions are investigated in a variety of scenarios. In addition, the verification procedure includes a list of observations made by human specialists. The effect of face mask occlusion on face verification performance is investigated in this research. Face mask synthesized datasets are generated using an augmentation method and could be utilized as training or testing datasets. On both the real-world and synthetic testing datasets, the proposed system achieves superior verification performance. We investigated the use of face attribute-based supervision for developing robust face detection, which is different from previous face detection re-search. Facial part detectors can be obtained without explicit part supervision from a CNN that has been trained on recognizing attributes from uncropped face images. ### Advantages of Current Study

Using ConvMixer and AdaBoost classifier for face recognition offers several advantages over other deep learning algorithms:

1. The ConvMixer blocks coupled with skip connections enable the extraction of intricate features from images, contributing to a more nuanced understanding of facial characteristics. 2. By incorporating well-established pre-trained CNN models as a backbone, such as ResNet-50, Inception-v3, and DenseNet-161, the system leverages the learned features from these models, enhancing its capacity to recognize faces effectively even with limited training data. 3. Different CNN models bring varied feature representations to the table. Integrating multiple models broadens the scope of extracted features, leading to more comprehensive and accurate recognition. 4. Robustness to Variability: Combining ConvMixer blocks, skip connections, and diverse pre-trained CNNs helps the system handle various challenges like occlusion, different lighting conditions, and pose variations, resulting in more robust and reliable face recognition. 5. The amalgamation of ConvMixer and pre-trained CNNs enhances the system's ability to generalize well to unseen faces, increasing its performance across different individuals and scenarios. 6. The architecture's flexibility allows for seamless integration of future advancements in both ConvMixer and pre-trained CNNs, ensuring the system stays up-to-date and continues to deliver accurate results for face recognition in case of occlusion. 7. By utilizing pre-trained CNNs, which have been trained on large datasets, the system saves training time and computational. Deep learning models are continuously evolving, and different architectures may perform better on certain datasets or domains. Nevertheless, the combination of ConvMixer with pretrained models, and AdaBoost presents a promising approach to address face recognition challenges, making it a valuable solution in the fields of urban security and video surveillance. ### Current Limitations and Future Work

The present work has some limitations that should be acknowledged. Firstly, the study utilized a relatively small dataset, which may not fully capture the diversity and complexity of real-world scenarios. Expanding the dataset size and including more diverse samples would enhance the generalizability of the findings. Additionally, the evaluation metrics used in this work, such as precision, recall, and detection accuracy, while informative, may not fully capture all aspects of face recognition performance, and the inclusion of other metrics, such as false acceptance rate (FAR) and false rejection rate (FRR), would provide a more comprehensive assessment. Moreover, the lack of a thorough comparison with existing state-of-the-art face recognition algorithms limits the ability to gauge the true superiority of the proposed ConvMixer and AdaBoost approaches. In future work, addressing these limitations is crucial to further improving the effectiveness and applicability of the proposed approach.