# Detection of Emotion Using Multi-Block Deep Learning in a Self-Management Interview App
## 3 Detection of Emotions Using Multi-Block Deep Learning in Self-Management Interviews
### Face Detection Using the Multi-Block Deep Learning Process  
For a deep learning model of the proposed user emotion extraction, this experiment extracted emotions using a DCNN based on the multi-blocked sample images of the major face areas, and the images with completed feature extractions, which was intended to minimize the performance time from entry, and the classification of the images. Figure 10 shows the multi-block deep learning structure proposed in this study. The emotion model was extracted by delivering a block target that included information about the features from the images learned by the DCNN in the multi-block and block selection stages. A convolution operation was conducted between the original images and the multi-block images extracted by sampling. This brought into relief the features of the major face areas for the extraction of emotions through the feature extraction filter. The filter coefficient for the feature extraction filter was set to a random value in the early stages, and was then set to the optimal filter coefficient with the least error rate through learning. Next, the process of reducing the images was executed, analyzing the features of the extracted images, and filtering the optimum features. At this time, the general DCNN launched a method for minimizing the cross-entropy loss function so it can be similar to the softmax result from image data entered from the multi-block and feature extraction stages. This study defines two cross-entropy loss functions like those in Equations (2) and (3) to deliver knowledge: In Equation (2), loss function \(L_{1}\) is a cross-entropy function based on a recognition result error for the label. In Equation (3), loss function \(L_{2}\) is the cross-entropy function representing the error with the block target representing the predicted probability value of the DCNN. Figure 10: The proposed multi-block deep learning structure. * The figure included in this image is the author, who agreed to provide the figure. \[L_{1} = -\sum_{n=1}^{|V|}H(y=n)\times logP(y=n\big{|}x;\ \theta) \tag{2}\] \[L_{2} = -\sum_{n=1}^{|V|}q(y=n\big{|}x;\ \theta_{E})\times logP(y=n \big{|}x;\ \theta) \tag{3}\]  
In the formula, \(q\) is the softmax probability value formed by learning the features of multi-blocked images, while \(P(y=n\big{|}x;\theta)\) is the probability the DCNN learned by utilizing the features of the whole images, \(n\) is the index of the feature category, and \(|v|\) is the total number of classes. This study used both the knowledge block target containing the feature information of the multi-block images delivered by the DCNN while learning, and the existing true class target value so as to allow learning everything. It extracted accurate emotions, utilizing feature extraction of the whole image area and the features of the blocked images of the key areas, giving a different weighted value to each of the two loss functions, \(L_{1}\) and \(L_{2}\), as seen in Equation (4):  
\[L=\ \alpha\ \times L_{1}+(1-\alpha)\times L_{2},\ 0<\alpha<1 \tag{4}\]  
In addition, for face area detection and estimation analysis, the CAS-PEAL face database was employed. The learning data in the database used consisted of classes of facial expression information for a total of 1040 persons, and consisted of a total of 1240 sheets of images for each class. The data for deep learning was composed of 10 sheets per emotion class, with noise added to the learning data. On the other hand, the AlexNet [35] structure, which is used a lot for facial recognition, was selected for comparison with the deep learning method proposed in this paper. Figure 11 shows a comparison of emotion recognition accuracy with the proposed method against the accuracy with AlexNet. The facial expressions were recognized through extraction of the entire face area and the main areas, and the accuracy of extracting emotions according to the expressions was compared, with the proposed method showing accuracy about 3.75% higher.