Abstract
A method for blink detection from video sequences gathered with a commercial camera is presented. This is used as a view-based remote eye gaze tracker (REGT) component performing two relevant functions, i.e. initialization and automatic updating in case of tracking failures. The method is based on frame differencing and eyes anthropometric properties. It has been tested on a publicly available database and results have been compared with algorithms found in literature. The obtained average true prediction rate is higher than 95%. The robustness of the automatic tracking failure detection has been tested on a set of experimental trials in different conditions, and yielded detection rates around 98%. The computational cost of the processing allows the blink detection algorithm to work in real time at 30 fps. The obtained results are in favour of combining blink detection with gaze mapping for the development of a robust view-based remote eye-gaze tracker to be introduced in different HCI contexts, specifically in the assistive technology framework.
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