Driving Alert System based on Facial Expression Recognition

Abstract

Abstract: Inattention is one of the major factors and causes of many road accidents and unforeseen crashes. As a result, it is vital to develop an automatic warning system for drivers, which will send timely warning signals to them. Based on the driver's facial expressions, this issue involves determining the driver's mental state. In applications such as driver warning systems, automated facial emotion recognition has become a recent development in the image processing world. There are existing methods for recognizing facial emotions regardless of whether the signal is noisy or imperfect data, but ultimately it lacks accuracy. It is also useless and not getting proper output in dealing with spontaneous feeling, and recognition. The proposed approach develops a driver warning system that extracts the facial expressions based on a novel efficient Local Octal Pattern (LOP) and effectively recognizes the facial expressions based on Deep Neural Networks, Convolutional Neural Networks (CNN). The LOP feature map provides as an input to CNN and guides in the selection of CNN learning data thereby improving and further enhancing the understanding and learning of CNN. It also has an ability to recognize both natural and spontaneous feeling, considered input as a image as well as video. The experimental results considering YawDD dataset indicates that the proposed system has been efficiently evaluated by considering the with metrics such as Precision, Recall and F-Score and thereby it is observed and inferred that the proposed system obtained a high recall rate of 96.09% in comparison with the other state-of-the-art methods