The assessment of anterior eye diseases and the understanding of psychological functions of blinking can benefit greatly from a validated blinking detection technology. In this work, we proposed an algorithm based on facial recognition built on current video processing technologies to automatically filter and analyze blinking movements. We compared electrooculography (EOG), the gold standard of blinking measurement, with manual video tape recording counting (mVTRc) and our proposed automated video tape recording analysis (aVTRa) in both static and dynamic conditions to validate our aVTRa method.
We measured blinking in both static condition, where the subject was sitting still with chin fixed on the table, and dynamic condition, where the subject's face was not fixed and natural communication was taking place between the subject and interviewer. We defined concordance of blinks between measurement methods as having less than 50 ms difference between eyes opening and closing.
The subjects consisted of seven healthy Japanese volunteers (3 male, four female) without significant eye disease with average age of 31.4±7.2. The concordance of EOG vs. aVTRa, EOG vs. mVTRc, and aVTRa vs. mVTRc (average±SD) were found to be 92.2±10.8%, 85.0±16.5%, and 99.6±1.0% in static conditions and 32.6±31.0%, 28.0±24.2%, and 98.5±2.7% in dynamic conditions, respectively.
In static conditions, we have found a high blink concordance rate between the proposed aVTRa versus EOG, and confirmed the validity of aVTRa in both static and dynamic conditions.