# 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  
It is necessary to compensate for senior drivers' weakened physical, perceptual, and decision-making abilities. It is also necessary to prevent secondary accidents, manage their health status, and take prompt action by predicting any potential traffic-accident risk, health risk, and risky behavior that might show up while driving. In cases where a senior driver's health status worsens due to a chronic disease, it becomes possible to recognize accident risks through facial expression changes. Accordingly, we propose resolving such issues with facial expression analysis using LFA-CRNN-based feature extraction for health-risk assessment of drivers. The LFA algorithm was performed to extract the characteristics of the driver's facial image in real time in the transportation support platform. An improved CRNN model is proposed, which can recognize the driver's face through the data calculated in this algorithm. Figure 2 shows the LFA-CRNN-based driving facial expression analysis for assessing driver health risks. Figure 1: Facial expression and bio-signal-based lifecare service. Figure 2: Line-segment feature analysis-convolutional recurrent neural network (LFA-CRNN)-based facial expression analysis for driver health risk assessment. The procedures for recognizing and processing a driver's facial expressions can be divided into detection, dimensionality reduction, and learning. The detection process is a step of extracting the core areas (the eyes, nose, and mouth) to analyze the driver's suffering condition. In the step, there is a preconditioning process to solve the problem that the core areas are not accurately recognized. To extract features from the main areas of frame-type facial images segmented from real-time streaming images, multiple AdaBoost-based input images are divided into blocks. In the dimensionality reduction process, the LFA algorithm reduces the learning and reasoning time by reducing data dimensionality (the number of pixels) in order to increase processing speeds to handle large-capacity original data. High-resolution data and the dimensionality of the input data are reduced. Lastly, in the learning process, drivers' facial expressions are recognized through the CRNN model designed to learn the LFA data. In addition, to confirm a driver's abnormal status based on the UNBC-McMaster shoulder pain expression database, the service proposed determines if the driver is in pain, identifies the driver's health-related risks, and alerts the driver to such risks through alarms. Real-Time Stream Image Data Pre-Processing for Facial Expression Recognition-Based Health Risk Extraction  
Because pre-existing deep-learning models utilize the overall facial image for facial recognition, areas such as the eyes, nose, and lips serving as the main factors for analyzing drivers' emotions and pain status are not accurately recognized. Accordingly, through a detection process module, pre-processing is conducted for dimensionality reduction and learning. To analyze the original data transferred through real-time streaming, input images are segmented at 85 fps, and to increase the recognition rate, the particular facial image sections required for facial expression recognition are extracted using the multi-block method [35]. In particular, in cases where a multi-block is big or small during the blocking process, pre-existing models are unable to accurately extract features from the main areas, and this causes significant errors relating to recognition and learning. To resolve such issues, multiple AdaBoost is utilized to set optimized blocking, and then sampling is conducted. Figure 3 shows the process of detecting particular facial areas. A Haar-based cascade classifier is used to detect the face; Haar-like features are selected to accurately extract the user's facial features, and the AdaBoost algorithm is used for training.