Deep Learning-Based Facial Emotion Recognition for Driver Healthcare

This study proposes deep learning-based facial emotion recognition (FER) for driver health care. The FER system will monitor the emotional state of the driver's face to identify the driver's negligence and provide immediate assistance for safety. This work uses a transfer learning-based framework for FER which will help in developing an in-vehicle driver assistance system. It implements transfer learning SqueezeNet 1.1 to classify different facial expressions. Data preprocessing techniques such as image resizing and data augmentation have been employed to improve performance. The experimental study uses static facial expressions publicly available on several benchmark databases such as CK+, KDEF, FER2013, and KMU-FED to evaluate the model's performance. The performance comparison only showed superiority over state-of-the-art technologies in the case of the KMU-FED database, i.e., maximum accuracy of 95.83 %, and the results showed comparable performance to the rest of the benchmark databases.