## 3 Materials and Methods
### System Implementation
Training: The time complexity for training involves multiple forward and backward passes through the ConvMixer model and updating the AdaBoost classifier, resulting in a higher time complexity. Inference: The time complexity for inference involves a forward pass through the ConvMixer model and making predictions using the AdaBoost classifier, resulting in a time complexity calculated approximately as:

\[\text{Time}-\text{Complexity}=\text{O}\Big{(}\text{L}\times\text{C}\times k^{ 2}\times\text{H}\times\text{W}+\text{M}\times\text{W}\Big{)} \tag{7}\]

## 5 Discussion

In this paper, DL algorithms are investigated for face recognition and verification in partially occluded environments where the object is not clearly visible, especially in real-time data acquisition. The most important part for object recognition is the face. The proposed approach for face recognition adopts a systematic and sophisticated strategy by breaking down the complex task into sub-problems and utilizing distinct visual cues and geometric features that are crucial in human face recognition. The initial step involves extracting various facial parts, such as eyes, eyebrows, nose, lips, gender, and age, from input images using specialized techniques for each feature. Subsequently, TL-based models are trained individually on these specific facial parts, allowing them to focus on learning relevant features for each component. To enhance accuracy and robustness, a weighted combination mechanism is employed to merge the outputs of these models. This combination takes into account the occluded portions of the face, giving more importance to the less occluded features and less importance to the occluded regions. By emulating human perceptual processes and leveraging deep learning's capacity for feature extraction and representation learning, this approach aims to achieve superior face recognition performance, particularly in handling occlusions and challenging facial variations. Empirical validation and comparative evaluation on suitable datasets would be essential to ascertaining the effectiveness of this approach. In this way, a pipeline of deep networks will be trained on different parts of the faces and later used for testing. Various TL algorithms have been trained on the eyes, nose, mouth, lips, and beard, and features have been extracted through these deep learning algorithms. Architecture has been shown in Figures 1 and 2. The proposed approach for face recognition tackles the challenge of dealing with occluded portions of the face in a systematic manner. The first step involves extracting non-occluded facial parts using a combination of various visual cues, and automatically clustered. By automating this step, the approach aims to discover an optimal face partition that captures essential features for recognition. In the second step, the approach focuses on identifying the occluded portions of the face in the images. Once occlusions are determined, the missing parts will be completed using integral imaging techniques. This completion process aims to reconstruct the occluded regions and make them available for subsequent recognition. The final recognition step involves using the completed non-occluded facial parts for face recognition. By leveraging the available information from the non-occluded regions, the approach seeks to improve recognition accuracy and reliability, even in the presence of occlusions. The design and implementation of this approach present several challenges. The selection of relevant visual cues and the development of automated procedures for face partitioning require careful consideration and experimentation. Additionally, finding effective methods to handle occlusions through integral imaging and integrating completed parts for recognition demand thorough investigation. Drawing insights from experimental psychology will guide the development of this approach, ensuring it aligns with human perceptual processes and maximizes recognition performance.