Eye blink completeness detection

Computer users often complain about eye discomfort caused by dry eye syndrome. This is sometimes caused and accompanied by incomplete blinks. There are several algorithms for eye blink detection, but none which would distinguish complete blinks from the incomplete ones. We introduce the first method which detects blink completeness. Blinks differ in speed and duration similar to speech, therefore Recurrent Neural Network (RNN) is used as a classifier due to its suitability for sequence-based features. We show that using unidirectional RNN with time shifting achieves higher performance compared to a bidirectional RNN, which is a suitable choice in this kind of problem where the feature pattern is not yet observed for the initial frames. We report the best results (increase by almost 8%) on the most challenging dataset: Researcher's night. We formulate a new important problem and state an initial benchmark for further research.