# Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions
## 1 Introduction
It is difficult to choose the appropriate feature parameters according to the field of application. Transformation can be made in various shapes, but it is necessary to determine the optimal feature parameters via experiential elements and various experiments, which is a problem. Recently, the deep-learning technique has widely been used. As the face recognition technique based on deep learning itself learns high-level characteristics, using a large amount of data built up in various environments, it shows a high recognition performance even in a wild environment. Accordingly, DeepFace (based on AlexNet) uses the locally connected convolution layer to effectively extract local characteristics from the face region [18]. DeepID proposed a lightweight convolutional neural network (CNN)-based face recognizer, using an input resolution with smaller pixels than DeepFace [19]. VGGFace, which appeared later, learned a deep network structure consisting of 15 convolution layers using a data set for high-capacity face recognition made by itself through an Internet search [20]. In addition, various studies were conducted to improve the performance of face recognition models such as DeepID2, DeepID3, and GoogLeNet [21; 22; 23]. As yet,if real-time video data are processed by a face recognition technique based on deep learning, it is necessary to learn many classes. Thus, they mostly show the structure in which a fully connected layer becomes larger, which accordingly decreases the batch size and acts as a factor disturbing convergence in the learning by the neural network. Accordingly, in this paper, to resolve such problems, facial expression analysis of drivers by using line-segment feature analysis-convolutional recurrent neural network (LFA-CRNN) feature extraction for health-risk assessment is proposed. A service using facial expression information to analyze drivers' health risks and alert them to risk-related matters is proposed. Drivers' real-time streaming images, along with deep learning-based pain expression recognition, were utilized to determine whether or not drivers are suffering from pain. When analyzing real-time streaming images, it may be difficult to extract accurate facial expression features if the image is shaking, and it may be difficult or impossible to run the analysis process on a real-time basis due to limited resources. Accordingly, a line-segment feature analysis (LFA) algorithm reduces learning and assessment time by reducing data dimensionality (the number of pixels). Also proposed is increasing the processing speed to handle large-capacity original data and high resolutions. Drivers' facial expressions are recognized through the CRNN model, which is designed to reduce input data dimensionality and to learn the LFA data. The driver's condition is understood based on the University of Northern British Columbia and McMaster University (UNBC-McMaster) database to understand the driver's abnormal condition. A service is proposed for coping with risks, spreading the dangerous conditions concerning health risks that may occur while driving through the notice by understanding the driver's conditions as suffering and non-suffering conditions. This study is organized as follows. Section 2 presents the trends in face analysis research and also describes the current risk-prediction systems and services using deep learning. Section 3 describes how the dimensionality-reducing LFA technique proposed in this paper is applied to the data generation process, and also presents the CRNN model designed for LFA data learning. Section 4 describes how the UNBC-McMaster database was used to conduct a performance test.