# Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions
## 3 Driver Health-Risk Analysis Using Facial Expression Recognition-Based LFA-CRNN
### LFA-CRNN Model for Driver Pain Status Analysis  
Once a feature map is generated, and the image is deduced through facial and contour line detection, the pre-processing of the given input images restructures them into two-dimensional arrays having a size of \(16\times 16\) through the LFA process. Specifically, the dimensionality is reduced through the LFA technique. Since LFA always has the same output size and consists of aggregate information on the line segment contained in the image, the reduced data themselves can be considered unique features. In addition, a learning model dedicated to LFA data is designed instead of a general CRNN learning model architecture for drivers' pain status, and the learning process is performed as well. Figure 8 shows the structure of the proposed LFA-CRNN model.  
The LFA-CRNN architecture is a CRNN learning model. It consists of one convolution layer, and expresses a feature map as sequence data through the reshaped layer. The features that changed into sequence data are transferred to the dense layer through two bidirectional gated recurrent units (BI-GRUs), and the sigmoid layer serves as the final layer before the results are output. Through the convolution layer's batch normalization (BN), the risk of depending on and overfitting the learning-speed improvement and initial weighted-value selection is reduced [36; 37; 38]. Since this learning model uses dimensionality-reduced LFA data, the compressed data themselves can be considered one feature. Accordingly, to express one major feature as a number of features in a convolution, the input-related expressions are diversely divided through a total of 64 filters having a size of \(16\times 16\). The value deduced through such a process passes through the BN and generates a series of feature maps through the rectified linear unit (ReLU) layer. Such feature maps are restructured through the reshape layer into 64 sequence data having a size of 256 and are used as the RNN model's input. The RNN model consists of two BI-GRUs, one with 64 nodes and one with 32 nodes. The data deduced through this process are delivered to the sigmoid layer through the dense layer. At this point, the dropout layer is arranged between the dense layer and the sigmoid layer to prevent calculation volume reduction and overfitting [39; 40; 41]. Lastly, through the Sigmoid class, nine types of pain are classified. In this model, the pooling layer generally used in the pre-existing CNN and CRNN models is not used. Since the input LFA data themselves have a considerably small size of \(16\times 16\), and consist of the cumulative number of line segments owned by the images when the involved data are compressed, the main features may be damaged or removed. In addition, in this model, BN and the dropout layer are arranged instead of the pooling layer, and the convolution's stride and padding are set to \(1\) and _same_, respectively. We used the convolution layer to get a variety of information about the expression of individual, highly-concentrated LFA data by designing the model like Figure 9. Thus, the filter of the convolution layer was set to \(16\times 16\) with stride = 1 and padding = \"same.\" Through this, one LFA data size is maintained, and because of the weighted value of the filter, it can express a  
Figure 8: The proposed LFA-CRNN architecture.  
lot of information. The data are used as input in each cycle of the RNN, and through the previous characteristics, strong characteristics are gradually detected from within.