Utilization of Facial Image Analysis Technology for Blink Detection: A Validation Study.
Momoko Kitazawa1, Michitaka Yoshimura, Kuo-Ching Liang
1Department of Ophthalmology (M.K., M.Y., L.K.-C., K.T.), Keio University School of Medicine, Tokyo, Japan; RIKEN (M.K., S.W.), Center for Advanced Photonics, Wako, Saitama, Japan; Department of Psychiatry (M.K., M.M., T.K.), Keio University School of Medicine, Tokyo, Japan; and Department of Psychiatry (T.K.), Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, NY.
Eye & Contact Lens
|June 27, 2018
Summary
A new automated video analysis method (aVTRa) accurately detects blinking movements. This technology validates well against electrooculography (EOG) in static conditions, aiding eye disease assessment and psychological studies.
Area of Science:
- Ophthalmology
- Computer Vision
- Psychology
Background:
- Blinking detection is crucial for assessing anterior eye diseases and understanding psychological functions.
- Existing methods require validation for accurate and automated blink analysis.
Purpose of the Study:
- To propose and validate an automated video analysis (aVTRa) algorithm for blink detection using facial recognition.
- To compare the aVTRa method with electrooculography (EOG) and manual video counting (mVTRc).
Main Methods:
- Developed an algorithm for automatic blink analysis based on facial recognition and video processing.
- Compared aVTRa with EOG (gold standard) and mVTRc in static and dynamic conditions.
- Defined blink concordance as <50 ms difference in eye opening/closing times.
Main Results:
- In static conditions, concordance was EOG vs. aVTRa (92.2±10.8%), EOG vs. mVTRc (85.0±16.5%), and aVTRa vs. mVTRc (99.6±1.0%).
- In dynamic conditions, concordance was EOG vs. aVTRa (32.6±31.0%), EOG vs. mVTRc (28.0±24.2%), and aVTRa vs. mVTRc (98.5±2.7%).
- Seven healthy volunteers participated, with average age 31.4±7.2.
Conclusions:
- The proposed automated video analysis (aVTRa) demonstrates high blink concordance with EOG in static conditions.
- The aVTRa method is validated for blink detection in both static and dynamic environments.
- This technology offers a promising tool for eye disease assessment and psychological studies of blinking.
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