## 3 Proposed Frameworks  
Our research focuses on a homogeneous ensemble of CNN models, which are trained and fine-tuned to specialize in recognizing specific facial expressions and 7 emotions. The homogeneous CNN ensemble approach uses a combination of deep learning models in the same category as CNN as DNN, EfficientNetB2, InceptionRestnetV2 enabling more robust and accurate emotion recognition. The architecture, as shown in Figure 1, details preprocessing for collecting the data from face expression detection, image processing and the training process of the HoE-CNN, highlighting the techniques used to enhance performance. The first necessary steps in facial detection methods are usually to preprocess the input original image, generate a face candidate box on the original image, and input the face candidate box into the network for feature extraction, for example, inputting an image into probability, Bx, By, Bh, Bw, Face and No Face classes. The following describes the two most widely used methods for generating face candidates. The first method is scanning the input image with different resolutions to obtain the candidate frame; this method can capture as large a position area of the face target as possible. The second method is to obtain the face candidate box by scaling the input image by a multiple of fixed scale. Second, face candidate frame generation is based on selective search. This method primarily uses some inherent attributes of the input object to determine the possible position of the face candidate frame in the input image. This method greatly reduces the number of face candidate frames and reduces the computing time of the network. In addition, multiresolution methods can be used to obtain regions of different scales, and the CNN model can then be used for classifying each region to yield the category and confidence. Therefore, the image processing could include cropping the face emotion area, transforming the image to greyscale, normalizing the values into [0, 1], and augmenting the data. In most cases, data augmentation uses a larger dataset to yield a more accurate prediction and a more robust model, which involves increasing the quantity of training data using information exclusively as training data to avoid overfitting on models. Data augmentation is performed during training to increase the amount of data, including rescaling from the original scale horizontal flip, random zoom, feature wise, rotation, horizontal, and vertical shifts as Figure 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Preprocessing** & **Class Labels** & **Algorithms** & **Type of Ensemble** \\ \hline [PERSON] and & & Angry, Disgust, Fear, & & \\ [PERSON] (2021) [25] & Augmentation & Happy, Neutral, Sad & VGGNet & None \\  & & and Surprise & & \\ [PERSON] and et.al. (2021) [26] & Face Detection, & Angry, Disgust, Fear, & & \\  & Residual Marking & Happy, Neutral, Sad & (Like-Ensemble) & Like-Homogeneous \\  & Block & and Surprise & & \\ [PERSON] and et al. & & Angry, Disgust, Fear, & & \\  & (2022) [27] & Face Detection & Happy, Neutral, Sad & (ResNet34v2 Backbone) & None \\  & & and Surprise & & \\ [PERSON] and et al. (2017) [28] & Extract key-points & Angry, Disgust, Fear, & & \\  & & and Surprise & & \\ Proposed Models & Face Detection, & Angry, Disgust, Fear, & & \\  & Augmentation, & Happy, Neutral, Sad & Ensemble CNN & Homogenous \\  & Normalizing Data & and Surprise & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of difference preprocessing operations, class labels, algorithms, and types of ensembles. Convolution neural networks (CNNs) have revolutionized the computer vision field. These networks are extremely effective for a variety of image and video recognition tasks due to their ability to automatically learn and extract key features from videos [29] or image [30; 31]. CNN has yielded other models in deep learning, indicating the potential of deep convolution neural networks.