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%.

  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