DRER: Deep Learning–Based Driver’s Real Emotion Recognizer

• DOI: 10.3390/s21062166
• Publication Date: 2021-03-22T03:47:41Z
• Authors: Geesung Oh; Junghwan Ryu; Euiseok Jeong; Ji Hyun Yang; Sungwook Hwang; Sangho Lee; Sejoon Lim
• Keywords: Facial expression recognition; Affective Computing

In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing emotional state one of most challenging and important tasks. Most previous studies focused on facial expression recognition state. However, while driving, many factors are preventing drivers from revealing emotions their faces. To address this problem, we propose a deep learning-based real emotion recognizer (DRER), which algorithm recognize drivers’ that cannot be completely identified based expressions. The proposed comprises two models: (i) model, refers state-of-the-art convolutional neural network structure; (ii) sensor fusion fuses recognized expressions with electrodermal activity, bio-physiological signal representing electrical characteristics skin, in even Hence, categorized conducted human-in-the-loop experiments acquire data. Experimental results show fusing approach achieves 114% increase accuracy compared using only 146% compare activity. conclusion, our method 86.8% induced driving situation.