Action Unit Analysis for Monitoring Drivers’ Emotional States

With the emergence of intelligent automotive systems, ensuring road safety has become paramount, necessitating the incorporation of sensors such as in-cabin cameras to comprehend drivers’ emotions. While emotion recognition through facial expression has demonstrated remarkable success in controlled laboratory settings, it often struggles to capture the complexities of real-world driving scenarios due to the lack of representative datasets. Facial action coding system (FACS) breaks down facial expressions into a combination of action units (AUs) and offers flexibility for more diverse facial expression categorization. In this study, we present an innovative framework designed to offer a solution to adapt machine learning (ML) models, including random forest (RF), gradient boosting (GB), and long short-term memory (LSTM) as a deep-learning model, originally trained on facial expression recognition (FER) datasets, to real-time monitoring of drivers’ emotional states. Notably, the LSTM model, trained on the CK+ dataset and tested on KMU-FED dataset, achieved the highest accuracy of 52.45%, while the best performance of the RF model reached 50.12% accuracy on the same datasets. The goal of this study is to utilize AUs as descriptors in our framework, aiming to rectify inconsistencies across different datasets. This approach ensures enhanced precision in recognizing drivers’ emotions within the vehicle cabin. Additionally, acknowledging the deployment limitations inherent in on-vehicle systems, our framework is designed to develop compact models, thereby reducing the computational load.