## 3 Proposed Methodology
### Feature Representation for Expression Classification  
Feature extraction is a crucial task to extract discriminating features from the input image \(\mathcal{F}_{n\times n}\) to ensure that the extracted feature contains more distinctive patterns [48]. Here, the input image may be a grayscaled or RGB color image. In the field of computer vision and in image processing research areas, feature extraction starts from an initial set of measured data and builds the features which are supposed to be informative and non-redundant, facilitating the subsequent learning and generalization steps. In many cases, the texture feature extraction techniques [49] lead to better human interpretations. Moreover, it is related to dimensionality reductions, i.e., when the input image size is  
Figure 3: Face pre-processing steps in the proposed FERS. too large to be processed as the representation for that image, it is transformed into a reduced set of features, also called a feature vector. The modern state-of-the-art technique for generic CNN feature representations for facial expression recognition problems could compete with the statistical and structural-based methods of computer vision. These generic CNN features can cope with the articulation and occlusion face images captured in an unconstrained environment and can achieve better performance. The proposed method describes a complex CNN baseline model with two components, i.e., feature extraction and classification parts. The proposed CNN model uses a convolutional neural network architecture with five to seven deep image perturbation layers. The model performs convolution operations using the ReLU activation function followed by max-pooling and batch normalization operations for feature extraction. Finally, two flatten layers, which are fully connected, are used for classification tasks on the extracted feature maps from the top of the layers. The performance for the proposed CNN has been increased through adding new levels by applying image augmentation and progressive image resizing methods. These also help the model to prevent the over-fitting and imbalanced data problem. Progressive image resizing methods support the model to avoid the use of excessive computational power. Considering only the pre-trained weights of the last few layers are being used, these weights have to be learned properly. We take advantage of image augmentation, batch normalization, the activation function, and regularization methods including the mix-up optimizer and label smoothing techniques. The convolution operation is the primary operator and main building block of a CNN architecture. The term convolution is a mathematical operation that combines two functions and generates a third function. Here, it is used to extract features from the images. In the case of a CNN model, the convolution operation is executed over the input image with the help of a \(t\times t\) sized kernel or filter and then generates feature maps. The convolution operation is performed by sliding the filter followed by non-linearity over the input. At every location, matrix multiplication is performed and sums the result onto the feature map. Finally, we used a fine-tuning of the parameters and fusion methods to enhance the performance of the proposed recognition system. Thus, the descriptions of the employed layers for the proposed CNN architecture are as follows:  
* _Convolution:-_ The Convolution layer is the core building block of a CNN model that performs most of the computation operations. Convolution is a linear matrix operation consisting of some set of kernels or filters \(\mathrm{W}_{t\times t}\). The kernel is a small-sized matrix of weights that slide over the input [50] and performs element-wise matrix multiplication. The convolution operation essentially performs dot products between some sets of learnable filters \(\mathrm{W}_{t\times t}\) and local regions of the input image \(\mathcal{F}_{n\times n}\), and produces an output matrix of dimension \(n^{\prime}\times n^{\prime}\).