## 3 Materials and Methods
### Features Classified Using AdaBoost Classifier  
The Adaboost [46] algorithm is an ML technique for FR that uses eigenvalues to extract features. The AdaBoost algorithm is used to develop a powerful learner over several rounds. AdaBoost creates a powerful learner by layering weak learners on top of one another. A new weak learner is added and a weighting vector is adjusted to focus on examples that were misclassified in previous rounds to produce a strong classifier employing numerous classifiers while training the dataset. Face recognition analysis is widely employed in a variety of applications. According to a review of the literature, various algorithms have been created to recognize faces. The AdaBoost method is a simple-to-implement algorithm that improves detection accuracy. As a result, this research evaluates an AdaBoost algorithm for human face recognition, see Algorithm 2. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Layer Name** & **Layer Type** & **Input Size** & **Output Size** \\ \hline Input Image & Input & (32, 32, 32, B) & (32, 32, 32, B) \\ Patch Split & Convolution (Stride = p, & (32, 32, 32, B) & (h, n/p, n/p, B) \\  & Kernel = p) & & \\ ConvMixer Block 1 & Alternating & (h, n/p, n/p, B) & (h, n/p, n/p, B) \\ Skip Connection 1 & Elementwise Addition & Same & Same \\ ConvMixer Block 2 & Alternating & Same & Same \\  & Convolutional Layers & Same & Same \\ Skip Connection 2 & Elementwise Addition & Same & Same \\ ConvMixer Block 3 & Alternating & Same & Same \\  & Convolutional Layers & Same & Same \\ Skip Connection 3 & Elementwise Addition & Same & Same \\ Global Pooling & Global Average Pooling & Same & Same \\ Flatten & Flatten & (h, 1, 1, B) & (h \(\times\) B,) \\ Dense Layer & Dense & (h \(\times\) B,) & (e, B) \\ SoftMax & Softmax & (e, B) & (num_classes, B) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ResNet-50 architectural view. All samples are equally weighted with Wi during the AdaBoost training phase. The weights are then repeatedly improved by raising the weights associated with misclassified data. To generate the final output of the boosted classifier, numerous weak learners can be combined in a weighted sum using the AdaBoost process. When compared with other commonly used classifiers such as neural networks and SVM, AdaBoost can achieve good classification performance with fewer parameter adjustments. We only choose a weak classifier for the specified classification problem and the number of boosting steps used in the training step when implementing AdaBoost. Each round of boosting can include many weak classifiers. At each round of boosting, the AdaBoost algorithm will choose the weakest classifier that gives the best results. The following are the major processes involved in implementing the AdaBoost algorithm. To implement it, we need decision stumps, which work on the principle of the AdaBoost classifier. The procedure is carried out three times. A linear combination of weak classifiers makes up the final classifier. AdaBoost must meet two requirements: (1) the classifier must be trained interactively on a variety of weighed training instances; and (2) the classifier must be trained on many weighed training examples. It seeks to minimize training errors in each iteration to produce a good fit for these samples. What is the mechanism behind the AdaBoost algorithm? The procedure is as follows: AdaBoost begins by randomly selecting a training subset. It trains the AdaBoost machine learning model iteratively by selecting the training set based on the previous training's accurate prediction. It gives incorrectly categorized observations a larger weight so that they have a higher chance of being classified correctly in the next iteration. Equation (6) represents this state as follows:  
\[S(x)=w_{1}s_{1}(x)+w_{2}s_{2}(x)+w_{3}s_{3}(x)+w_{4}s_{4}(x) \tag{6}\]  
where S is a string classifier, w is the weight parameter, s\({}_{1}\) and s\({}_{2}\) are weak classifiers and x is a feature vector in Equation (6).