## 3 Materials and Methods
### System Implementation
After each iteration, ConvMixer predictions are calculated, and sample weights are adjusted based on errors. The AdaBoost algorithm then takes the lead, updating sample weights to focus on misclassified samples and calculating alpha values for weak classifiers. When it is time for face recognition, we apply the pre-trained CNN models to extract features from the testing images. AdaBoost, being an ensemble learning technique, combines the weighted votes from the weak classifiers to make predictions. The result is a predicted label for each testing image. Finally, we evaluate the system's performance. By comparing the predicted labels to the actual labels of the testing images, we compute crucial metrics, including accuracy, precision, recall, and the F1-score. These metrics provide insights into how effectively our algorithm recognizes faces, even in situations involving partial obstruction. As a result of rigorous training, evaluation, and parameter tuning, our Automated Face Recognition system achieves an impressive accuracy of around 97%. The innovative blend of ConvMixer's feature extraction, the expertise of pre-trained CNN models, and the ensemble predictions of AdaBoost culminate in a powerful solution that outperforms conventional methods. This development underscores the potential of this new approach in the realm of facial recognition, making possible improved accuracy and robustness against challenging scenarios. ## 4 Results

### Environmental Setup

To achieve high performance with the proposed AFR-Conv-Ada method, the model required a large dataset. Moreover, due to the overfitting issue, the architecture's performance degraded with a small dataset, with the network performing well on a training set but poorly on test data. The data augmentation method is used in this study to enlarge the dataset and alleviate the overfitting problem. As a result of the data augmentation approach that employs fundamental image processing methodology, the dataset size is increased. The Google Colab platform is used to execute the implementation, which is based on the PyTorch deep learning framework and runs on two NVIDIA 2080 ti (12 GB) GPUs. The batch size in training is set at 128, and the training process takes 32K iterations to complete. We extract the 512-dimension attributes for each normalized face in testing. We apply data augmentation to the training set, such as flipping data, to reduce overfitting and increase the generalization of the trained models. All the images are preprocessed using the Viola-Jones method, and then the extracted parts are selected and stored in a database before the feature extraction step. In the training dataset, 30% of the face images are allocated to training the classifier, and 70% of the images are used to test the recognition of the proposed system. ### Data Augmentation for Class Imbalance

The optimization method is a stochastic gradient descent SGD + momentum (0.9) with momentum. The batch size is 256. Regularization: The weight decay is 5 \(\times\) 10\({}^{-4}\), and L2 regularization is employed. After the first two completely linked layers (\(p\) = 0.5), dropout occurs. Even though ResNet50 is deeper and has more parameters, we believe it can converge in fewer cycles for two reasons: first, the increased depth and smaller convolutions introduce implicit regularization; second, there are several layers of pre-training. Initialization of parameters: For a shallow network, parameters are initialized at random. The weight w is sampled from N (0, 0.01), and the bias is set to 0. The first four convolutional layers and three fully connected layers are then initialized using the parameters of the A network for deeper networks. It was later discovered, however, that it is also feasible to directly initialize it without the need for pre-trained parameters. Eachrescaled image is randomly cropped in each SGD iteration to generate a 224 \(\times\) 224 input image. The cropped image is additionally randomly flipped horizontally and RGB color altered to improve the dataset.