## 3 Materials and Methods
### Proposed ConvMixer Learning Model  
The AFR-Conv system is developed in this paper based on the trained ResNet-50 model. The AFR-Conv system architecture is depicted in Figure 1. For object recognition, a novel architecture based on depthwise separable convolutions was recently proposed. [PERSON] et al. 's MLP-Mixer model [44; 45] was used as inspiration for the architecture. To be more specific, a depthwise convolution is used to mix spatial locations before a pointwise convolution is used to combine channel locations. Figure 5 shows the ConvMixer blocks' modified version of the original ConvMixer layer. The batch normalization operation and activation layers are switched in order from the original version. We also utilize ReLU instead of GELU to activate all layers. The DSC offers two advantages when it comes to constructing a deep learning model: (1) It might be able to reduce the number of parameters, and (2) it may be used to improve model generalization. Thus, DSC was found to improve training efficiency and classification accuracy. The ConvMixer architecture tries to prove that the superiority of the ViT is partly due to using image patches and introduces a novel ConvMixer model that is similar to the ViT as well as the MLP-Mixer model. It works directly with patches as input, isolates the mixing of spatial and channel dimensions, and keeps the network's size and resolution constant, but it utilizes convolutions to achieve the mixing steps. It gives better validation accuracy compared with a basic CNN model with four times fewer parameters. It also uses batch normalizations instead of layer normalizations. The ConvMixer model is a recent approach that highlights the power of processing images in patches to achieve impressive performance on various tasks. Its architecture consists of first splitting input images, each of 32 \(\times\) 32 pixels with three RGB channels, into different patches, enabling local information processing. The crux of ConvMixer lies in the alternating application of convolutional networks along the channel-wise and space-wise dimensions of these patches. This approach allows the model to capture cross-channel interactions and local spatial relationships effectively. Without the need for recurrent layers or self-attention mechanisms, ConvMixer demonstrates remarkable results by assembling basic building blocks like convolutions, non-linearities, batch normalizations, mean pooling, and dense layers in different architectures. This simple yet potent model sheds light on the significance of patch representations for high-performance image understanding and classification tasks. Further insights and specific architectural details can be found in the original ConvMixer paper. The architecture of ConvMixer is summarized in Figure 5.  
Figure 4: A visual example of VGG-16 architecture used for ConvMixer architecture. The main concept of the ConvMixer architecture is to begin by splitting the input image into patches of size (p, p) using a convolutional layer with the stride argument. The stride determines how the convolutional kernel moves across the input image. If the stride is set to 1, the convolutional kernel is applied around every pixel in the image, resulting in overlapping patches. The overlap occurs because the kernel moves one pixel at a time, covering neighboring regions. On the other hand, if the stride is set to a value greater than 1 (e.g., stride = 2), the convolutional kernel skips pixels, only applying the convolution to every other pixel. As a result, the patches become non-overlapping and cover the image in a grid-like fashion. When stride = p, the convolutional kernel moves p pixels at a time, leading to disjointed and adjacent windows. These windows cover the entire image in non-overlapping patches of size (p, p). Each patch is then processed  
Figure 5: The proposed automated face recognition (AFR) method is based on a residual connection with ConvMixer to extract features and classify them by AdaBoost.