Abstract: Mental health is one of the most neglected areas in healthcare systems around the world. Issues like depression, anxiety, ADHD, and chronic stress affect millions of people, yet a large number never get screened or diagnosed. One core reason is that most current detection approaches rely heavily on self- reported information, which is often unreliable when people feel too embarrassed, are in denial, or simply lack awareness of their own condition. To address this, we built NeuroEye — a browser-based tool that tracks eye activity passively during a session to detect early signs of mental distress. Instead of asking users to fill out questionnaires, NeuroEye uses a regular webcam to observe natural eye behaviour in the background. It uses the MediaPipe Face Mesh library to detect facial landmarks and calculate the Eye Aspect Ratio (EAR) for monitoring blinks in real time. Gaze tracking is done by monitoring iris position across nine zones. These signals are then compared against known clinical thresholds to identify possible indicators of stress, low mood, fatigue, or attention issues. All processing happens locally on the user's device and no data is sent to any server, keeping everything completely private. NeuroEye is a screening support tool and is not a replacement for professional diagnosis.

Keywords: Affective Computing, Eye Aspect Ratio (EAR), MediaPipe Face Mesh, Mental Health Screening, Ocular Biometrics, Gaze Estimation, Non-invasive Assessment

Downloads: | DOI: 10.17148/IARJSET.2026.13451