Abstract
In this paper some modifications of the eye blink detection method based on the weighted gradients are presented. We propose some novel techniques of denoising of the obtained waveforms and fully automated zero-crossing detection capable to detect eye blinks. These modifications were tested on two different databases. The evaluation of results show that the introduced modifications improve performance of the proposed detection framework, in which the pixels of each video frame are divided into two groups according to the direction and magnitude of the hybrid gradient vectors. The distance between their centers of gravity is used for the determination of the eye movement characteristics. The proposed technique can also be used for the analysis of eye movements and can be utilized in systems which are monitoring human fatigue, drowsiness and emotional states.
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Radlak, K., Smolka, B. (2013). Blink Detection Based on the Weighted Gradient Descriptor. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_68
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DOI: https://doi.org/10.1007/978-3-319-00969-8_68
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