Computer vision syndrome prevention: detection of expression and eye distance with monitor screens

Abstract

Computer vision syndrome (CVS) is a vision-related complaint caused by computer usage. CVS can be analyzed through facial expressions detected by a camera. Expression detection is categorized into two groups: safe and dangerous. The safe category comprises happy, neutral, disgusted, sad, angry, and surprised, while the dangerous category includes sad and fearful emotions. This division is based on the similarity of CVS symptoms to facial emotion characteristics. Additionally, an additional feature is implemented to detect the distance between the screen and the user's eyes using the FaceMeshModule to prevent the user's eyes from getting too close to the screen. Both detections will provide warning notifications when a dangerous category expression is detected ≥70% every minute, and when the distance between the screen and the eyes is ≤40 cm. Notifications in this program use the Tkinter library as a graphical user interface (GUI) message box. In this research, facial expressions are detected using the CascadeClassifier for face detection and the extreme inception (Xception) as the facial expression classifier. The results of expression detection achieved an accuracy of 94%, an F1-score of 94%, precision of 95%, and recall of 94%.