# Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions
## 5 Conclusions  
With this paper's proposed method, health risks due to an abnormal health status that may occur while someone is driving are determined through facial expressions, a representative medium capable of confirming a person's emotional state based on external clues. The purpose of this study was to construct a system capable of preventing traffic accidents and secondary accidents resulting from chronic diseases, which are increasing as our society ages. Although automated driving systems are being mounted on vehicles and are commercialized based on vehicle technology advancements, such systems do not take into consideration driver status. If abnormal health status in a driver is detected while the vehicle is in motion, it may operate normally, but the drivers might not be able to meet the required \"golden time\" to address any health problem that arises. Our system checks the driver's health status based on facial expressions in order to resolve to a certain extent problems related to chronic diseases. To do so, in this paper, an LFA dimensionality reduction algorithm was used to reduce the size of input images, and the LFA-CRNN model receiving the reduced LFA data as input was designed and used to classify the status of drivers as being in pain or not. The LFA is a method where a series of filters is used to assign a unique number to the line-segment information that makes up a facial image, and then, the input image is converted into a two-dimensional array having a size of \(16\times 16\) by adding up the unique numbers. As the converted data are learned through the LFA-CRNN model, facial expressions indicating pain are classified. To evaluate performance, a comparison was made with pre-existing CRNN and AlexNet models. The UNBC-McMaster database was used to learn pain-related expressions. As far as the accuracy and loss calculated through learning are concerned, the LFA-CRNN showed the highest accuracy at 98.92%, a CRNN alone showed accuracy of 98.21%, and AlexNet showed accuracy of 97.4%. In addition, the LFA-CRNN showed the lowest loss at approximately 0.036, the CRNN showed a loss of 0.045, and AlexNet showed a loss of 0.117. Although the LFA-CRNN's precision and recall measurement results were quite poor, average precision was 75%, which is as high as the 75% precision achieved by AlexNet.  
We optimized the facial expressions and the data sources for the LFA-CRNN, and intend to compare the processing times of several models and improve the accuracy in the future. The proposed LFA-CRNN algorithm shows high dependency on the outline detection method. This is self-evident, because LFA is based on segment analysis. We are devising an outline detection technique that can optimally be applied to LFA based on this fact. In addition, the LFA performance process generates a one-dimensional sequence before the production of a two-dimensional LS-Map. It is expected that by converting this, a class can be produced that can be used in the neural network model. Through this improvement process, we will combine the LFA-CRNN model with a system for recognition of facial expressions and motions that can be used in services like smart homes and smart health care, and we plan to apply that to mobile edge computing systems and video security.  
[PERSON] and [PERSON] conceived and designed the framework. [PERSON] and [PERSON]-M.K. implemented LFA-CRNN model. [PERSON] and [PERSON]-M.K. performed experiments and analyzed the results. All authors have contributed in writing and proofreading the paper. All authors have read and agreed to the published version of the manuscript. This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 CTAP-C157011-01). The authors declare no conflict of interest.