Driver's facial expression recognition based on MobileNetV2 from edge impulse.

Facial expression is an important basis for judging human emotions. The driver's mood can be accurately determined by recognizing their facial expression. Because the facial expression data set based on ordinary life scenes cannot accurately show the relationship between the driver's facial expression and his psychological state in the real driving environment. This paper uses 1000 facial expression images of drivers based in the real driving environment. These images include 6 different facial expressions to express 6 different emotions. This paper uses Edge Impulse to train several neural network models capable of recognizing these 6 different facial expressions and to realize the purpose of judging the driver's psychological state. From the experiment results, the MobileNetV2 model used has the highest accuracy rate of 99.1% on training data and 97.4% on testing data. This shows that the lightweight neural network model MobileNetV2 can accurately recognize the driver's facial expressions in the real driving environment. [ABSTRACT FROM AUTHOR]