Accurately recognizing driver emotions through using CNN fused features and NasNet-large model

This research endeavours to enhance road safety by developing an accurate driver emotion recognition system. A novel model is introduced, incorporating transfer learning principles alongside NasNet-Large CNN and Faster R-CNN, specifically designed for Driver Facial Expression (DFE) analysis. The primary objective is to bolster the recognition accuracy of Driver Facial Expression Recognition (DFER). A noteworthy improvement in the accuracy and efficiency of facial detection is attained by customizing the Faster R-CNN learning module with the Inception V3 model. The capability to accurately detect emotions is of paramount importance, as it facilitates timely interventions to avert potential accidents. To address the challenges associated with DFER accuracy in low-resolution images, this research deploys a myriad of deep learning methodologies. Through a meticulous analysis, the study identifies and implements feasible and superior solutions to enhance DFER accuracy. Additionally, the inherent constraints of low-resolution images are mitigated through the strategic application of data augmentation techniques. The evaluation of this research showcases impressive accuracy levels across diverse datasets, including JAFFE, CK+ , FER-2013, and DFERCD. These findings bear substantial implications for enhancing Advanced Driver Assistance Systems (ADAS) and contribute substantially to the overarching realm of road safety.