Vision-Autocorrect: A Self-Adapting Approach towards Relieving Eye-Strain Using Facial-Expression Recognition

Abstract

The last two years have seen a rapid rise in the duration of time that both adults and children spend on screens, driven by the recent COVID-19 health pandemic. A key adverse effect is digital eye strain (DES). Recent trends in human-computer interaction and user experience have proposed voice or gesture-guided designs that present more effective and less intrusive automated solutions. These approaches inspired the design of a solution that uses facial expression recognition (FER) techniques to detect DES and autonomously adapt the application to enhance the user’s experience. This study sourced and adapted popular open FER datasets for DES studies, trained convolutional neural network models for DES expression recognition, and designed a self-adaptive solution as a proof of concept. Initial experimental results yielded a model with an accuracy of 77% and resulted in the adaptation of the user application based on the FER classification results. We also provide the developed application, model source code, and adapted dataset used for further improvements in the area. Future work should focus on detecting posture, ergonomics, or distance from the screen.