Abstract
Visual display terminal (VDT) syndrome causes dry eyes and related symptoms. In addition, the number of eye blinks is reduced in VDT workers. We have therefore developed a real-time eye blink counter system. This system employs face pattern recognition, eye-area detection, and tracking using a template-matching method for eye blink detection. The changes between successive frames in a movie captured by a web camera (30 fps, 320 × 240 pixels) were recorded. We then applied three different methods as image processing techniques for eye blink detection: the subtraction of successive frames, eye open/closed status detection by image scanning, and iris detection by template matching. The results showed that comparable performance could be achieved using all three methods. The system functioned well as a real-time eye blink counter and may also prove to be useful as a sleepiness or catnap detector, as well as find applications in man-machine interfaces and visual axis analysis.
