Facial Landmark Analysis for Detecting Visual Impairment in Mobile LogMAR Test

Area of Science:

• Ophthalmology
• Biomedical Engineering
• Computer Science

Background:

• Visual impairment is a significant global health concern requiring timely intervention.
• Smartphones can capture facial gestures, potentially indicating vision changes like squinting.
• Current vision tests are often conducted in clinical settings, limiting continuous monitoring.

Purpose of the Study:

• To develop and evaluate a mobile health application for digital vision testing using facial feature analysis.
• To identify specific facial landmarks that correlate with reading performance during a vision test.
• To explore the potential of smartphone-based facial tracking for unobtrusive vision assessment.

Main Methods:

• A mobile application was created to perform a digital logMAR test and record facial features simultaneously.
• 37 participants completed the mobile eye test.
• Facial landmarks were analyzed using exploratory data analysis and receiver operating characteristic (ROC) curves to assess discriminative power.

Main Results:

• Facial regions including the nose, mouth, and cheeks showed significant changes with reduced reading performance.
• Cheek squinting demonstrated high discriminative power, with an area under the curve (AUC) of 0.82.
• Specific facial landmarks can distinguish between correct, partial, and incorrect letter readings.

Conclusions:

• Tracking facial features via smartphone offers a promising method for continuous and unobtrusive vision assessment.
• Facial feature analysis integrated into everyday applications could facilitate widespread vision screening.
• Further large-scale studies in non-standardized environments are warranted to validate these findings.