# A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems
## 3 Experimental Results and Analysis  
In this study, two experiments were designed, and the first one utilized the JAFFE dataset, which is available in the literature and is frequently used in facial expression classification problems. Access to the Jaffe dataset is publicly available [27]. It consists of 213 black-and-white facial photographs of 10 Japanese women with 7 basic emotional expressions. Each participant displayed each emotional expression more than once. The workflow of the algorithm developed for use in facial expression classification problems is shown in Figure 4.  
During preprocessing, image intensities were normalized, and only the region containing the face expressing the emotion was resized. We resized the original image of size 256 \(\times\) 256 to 168 \(\times\) 120 by removing background effects. An example is presented in Figure 6.  
Figure 4: The architectural design of the facial expression classification system. Figure 5: Sample images from the Jaffe database [27] for facial expression recognition. Additionally, histogram equalization was applied to remove lighting effects. For feature extraction, we employed the local binary pattern (LBP) method. To evaluate the effectiveness of LBP, we also tested the histogram of oriented gradients (HOG) method for comparison. The output image (Figure 7) shows that the LBP-processed image has improved the facial landmark boundaries. Moreover, it is very important to analyze the changes in the pixel positions of key regions such as the eyebrows, eyes, and mouth with respect to a given reference point in facial expression detection. We also test other feature representations such as HOG for comparison. For the classification phase of the facial expression recognition problem, the architecture (network structure) of the proposed algorithm needs to be established. In the developed algorithm, the wavelet coefficients are randomly generated and remain constant. In addition, the initial parameters are all user inputs and are randomly defined. At this stage, the structure of the network is created. Figure 6: Example of image resizing used for preprocessing. Figure 7: Example of local binary pattern (LBP) processing in feature extraction. In this study, the structure of the network was defined with user input. Accordingly, the number of neurons/ fuzzy rules in the HL was set as 10, and the minimum error was set as 0.001. To calculate the average classification measure, the experiment was performed with 10-fold cross-validation. Because the number of images in the dataset is limited, after training the dataset, the test image is classified using the proposed hybrid algorithm classifier. All analyses were performed using MATLAB R2021b. When a dataset contains more than two classes or an unequal distribution of observations across classes, the calculated accuracy rate may be misleading. Therefore, in addition to accuracy, a confusion matrix was generated to summarize the classification algorithm's performance. The confusion matrix provides preliminary information about the types of errors made by the model. Table 1 provides a detailed confusion matrix showing classification performance across various facial expressions. According to Table 1, the performance of the classifier for the expression of happiness is 96.13%. The performance is 91.89% for fear, and 97.63% for surprise. It was determined that over 90% of the classifications for all expressions performed by the proposed algorithm were correct. The average classification accuracy was calculated as 94.55%. In parallel, the experiment was repeated on the same dataset using different methods in the face feature extraction step. Thus, the aim was to observe how the proposed algorithm performs with different facial feature extraction algorithms. In the experiment, HOG was used in the face feature extraction step. In the classification phase, the FWMHKELM algorithm was applied, and the confusion matrix was obtained again.