期刊
COMPUTER VISION AND IMAGE UNDERSTANDING
卷 176, 期 -, 页码 78-85出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cviu.2018.09.006
关键词
Blink detection; Incomplete blinks; Complete blinks; Recurrent neural network time shifting
资金
- Slovakian Grant [VEGA 1/0874/17]
- Research and Development Operational Programme [ITMS 26240220084]
- European Regional Development Fund
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.
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