Gaze gestures bear potential for user input with mobile
		  devices, especially smart glasses, due to being always
		  available and hands-free. So far, gaze gesture recognition
		  approaches have utilized open-eye movements only and
		  disregarded closed-eye movements. This paper is a first
		  investigation of the feasibility of detecting and
		  recognizing closed-eye gaze gestures from close-up optical
		  sources, e.g. eye-facing cameras embedded in smart
		  glasses. We propose four different closed-eye gaze gesture
		  protocols, which extend the alphabet of existing open-eye
		  gaze gesture approaches. We further propose a methodology
		  for detecting and extracting the corresponding closed-eye
		  movements with full optical flow, time series processing,
		  and machine learning. In the evaluation of the four
		  protocols we find closed-eye gaze gestures to be detected
		  82.8%-91.6% of the time, and extracted gestures to be
		  recognized correctly with an accuracy of 92.9%-99.2%.<br>
<br><pre>@inproceedings{Findling_19_ClosedEyeGaze,
  author = {Findling, Rainhard Dieter and Nguyen, Le Ngu and Sigg, Stephan},
  booktitle = {15th International Work-Conference on Artificial Neural
  		  Networks ({IWANN} 2019)},
  title = {Closed-Eye Gaze Gestures: Detection and Recognition of
  		  Closed-Eye Movements with Cameras in Smart Glasses},
  year = {2019},
  month = jun,
  pages = {322--334},
  publisher = {Springer},
  series = {LNCS},
  volume = {11506},
  keywords = {Closed eyes, Gaze gestures, Machine learning, Mobile
  		  computing, Recognition, Smart glasses, Time series
  		  analysis}
}
</pre>