Abstract:
One of the most important body part is the face which holds a lot of information. Any person's
mental state is revealed through their facial expression.The purpose of the study is to develop a
system that can ensure safe driving with great accuracy. The main objectives of this study is to
detect whether the driver is asleep or not, avoid road accidents, eliminate reckless driving, alert
drivers about their mental and emotional situation. For this research, I have collected data from
two different datasets. One is FER-2013 and another one is drowsiness dataset for open eyes and
closed eyes. The images of the emotion dataset contains only the face which are enough cropped
and the drowsiness dataset contains only the eyes. I have used angry, fear, happy, neutral, sad and
yawning for emotion classifications. I have used 4 deep learning models in this research. The 4
four models are Xception, InceptionV3, ResNet50 and VGG19. These neural network models are
used for feature extraction and classification tasks. The model that gives the higher accuracy than
other models is Xception. In both tasks, The Xception outperformed the competition. For eye
detection, it obtained 98.97% accuracy, and for face emotion detection, 99.26% accuracy. It
showed excellent accuracy and metrics when it came to classifying emotions. The model performs
quite well, with an average precision, recall, and F1-score of about 0.99. Overall, Xception
performed exceptionally well across a number of emotion classifications and attained 99%
accuracy on the eye dataset. The weighted and macro averages both confirmed the effectiveness
of the system. This study suggests an improved pretrained model based approach for detecting
driver’s inattention, which will ensure safe driving with great accuracy and improve the drivers
driving efficiency. In future, I will work on adding night vision capabilities, the system will be
able to identify and recognize objects better and adapt to different driving circumstances more
easily. Furthermore, it may be proposed that driving behaviors like speeding, safe driving, and
braking suggest a more advanced system. The combination of these factors can result in an
improved and more advanced system that can identify and mark drowsy drivers, improve drivers'
concentration, and reduce the number of traffic accidents.
