# Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions
## 1 Introduction  
In our lives: emotion is an essential means to deliver information among people. Emotional expressions can be classified in one of two ways: verbal (the spoken and written word) and non-verbal(gestures, facial expressions, etc.) [1; 2]. People communicate with others every day, and in such a process, facial expressions account for a significantly high proportion of meaning. Expressions can be used to accurately understand another person's emotional state, and since the perception of the emotion in facial expressions is an essential factor of social cognition, it could be seen as playing an essential role in diverse areas of life [3; 4]. As described, facial expressions can be used to understand and empathize with other people's emotions. A number of services and prediction models for analyzing such expressions and understanding users' emotional states have been, and are being, studied [5; 6]. Recently studied are lifecare services using gesture recognition or expression analysis to detect the risks to which the elderly and patients with chronic diseases are exposed. Currently, the whole world is entering an aging society, and accordingly, the number of patients with chronic diseases (hypertension, cardiovascular diseases, and coronary artery disease, etc.) is increasing. In addition, even in the low age group, the prevalence of chronic diseases increases due to changes in dietary habits (food with high calories and high sugar content, etc. ), lack of exercise, and smoking, etc. [7; 8]. From one generation to the next, humankind will require services to continuously monitor and manage chronic diseases. Unless medical service technology makes innovative progress, the demand for such services will continue to increase. There are many cases where the elderly or patients with diseases experience emergencies, major accidents, or death from disease-related reasons such as acute shock. In particular, since car accidents frequently occur due to acute diseases while someone is driving, it is necessary to take urgent action to prevent them [9; 10; 11]. When such accidents occur in the absence of a fellow passenger, the driver is unable to take prompt action. Moreover, as autonomous vehicles become more popular, they can operate on cruise control regardless of the status of the driver. In such cases, even after a driver's abnormal health status is detected, he or she might not be able to do something within the so-called \"golden time\" to address the problem. Although the mortality of patients with chronic diseases has decreased due to medical progress, it is still necessary to continuously manage and prepare for emergency situations [12; 13]. To that end, services are being studied that alert friends, hospitals, police stations, etc., after detecting a driver at risk. However, since such studies involved prediction models based on external factor analysis, a driver's potential risk factors have not been applied, and it is difficult to predict accidents that occur due to internal factors. For prediction services in which internal factors apply, accurate prediction requires a particular device to be installed in the vehicle, and a given code of conduct is to be followed. As for the possibility of checking the driver's internal risk factors intuitively, it is possible to judge a dangerous situation through the driver's facial expressions. Thus, this study was conducted for the prediction of risk through recognition of the driver's facial expressions. Concerning current recognition of facial expressions, various studies are in progress [14]. As for the traditional face recognition techniques, classification models using the extraction of handcrafted features (local binary pattern (LBP), histogram of gradients (HOG), gabor, scale-invariant feature transform (SIFT)) for the sensible extraction of the characteristics of face images have usually been used [15; 16; 17]. These methods have a problem, however, in which the performance deteriorates when there are various changes in the face in an actual environment.