## 3 Materials and Methods
### System Implementation  
The AFR-Conv system outlines an approach for automated face recognition that combines multiple advanced techniques to achieve accurate results. These steps are described in Algorithm 3. It begins by initializing parameters, including the number of ConvMixer blocks and AdaBoost iterations, and selecting powerful pre-trained CNN models such as ResNet-50, Inception-v3, and DenseNet-161. The preprocessing step prepares the training and testing images for analysis. The algorithm then sets up the ConvMixer architecture, including ConvMixer blocks and skip connections. AdaBoost is initialized with sample weights and weak classifiers. During the training phase, ConvMixer models are trained iteratively on the training data. Predictions are made using AdaBoost, sample weights are adjusted based on classification errors and alpha values are calculated for weak classifiers. The algorithm also leverages the strengths of pre-trained CNNs by extracting features using ResNet-50, Inception-v3, and DenseNet-161. The AdaBoost classifier combines these features' weighted votes to predict labels for testing images. In the evaluation phase, the algorithm assesses the predicted labels' accuracy and performance metrics. This approach effectively combines ConvMixer, pre-trained CNNs, and AdaBoost to create a robust face recognition system that takes advantage of transfer learning (TL), handles occlusion, and produces accurate predictions. The algorithm's comprehensive methodology holds potential for improving facial recognition outcomes in real-world scenarios. The algorithm's effectiveness hinges on a set of pivotal settings and configurations that tailor its behavior and performance. At its core, the algorithm revolves around key parameters, including the number of ConvMixer blocks for feature extraction and the iterations for the AdaBoost algorithm to refine predictions. By design, it harnesses the capabilities of potent pre-trained CNN models--namely, ResNet-50, Inception-v3, and DenseNet-161--to extract intricate features from images. The preprocessing step encompasses essential transformations such as image resizing and normalization, readying the training and testing images for subsequent analysis. In the heart of the algorithm, the ConvMixer architecture materializes with a specified count of ConvMixer blocks, complemented by strategic skip connections. ``` Input: Training images with labels, Testing images, Number of ConvMixer blocks (num_blocks), Number of boosting iterations (num_boosting_iterations) Output: Accuracy, F1-score, Precision, Recall metrics Step 2 Parameters Setup: num_blocks \(\leftarrow\) 3, num_boosting_iterations \(\leftarrow\) 5 Preprocessing: Define image preprocessing transformation: Resize images to (224, 224) and convert to tensor and normalize pixel values Step 4 Pre-trained CNN Initialization: pretrained_models \(\leftarrow\) [ResNet-50, Inception-v3, DenseNet-161] Freeze all parameters in pretrained_models ConvMixer Model: Define ConvMixerBlock class and Define ConvMixer block layers Step 5 (a) Define ConvMixerArchitecture class: Define ConvMixer architecture with ConvMixer blocks and skip connections (b) Initialize conv_mixer_model as ConvMixerArchitecture()  AdaBoost Initialization: Initialize sample_weights with equal weights for training samples, initialize \(\text{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{\small{{\small{\small{\small{{\small{{\small{\{{\small{{



inclusion of skip connections, the architecture is primed to capture intricate features from facial images, crucial for accurate recognition. The AdaBoost component is initialized by assigning equal weights to all training samples and preparing weak classifiers, such as decision stumps. As we delve into the training phase, the ConvMixer model learns iteratively from the training data.