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Neural Network Based Eye Tracking

  • Pavel Morozkin
  • Marc Swynghedauw
  • Maria Trocan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)

Abstract

The EyeDee embedded eye tracking solution developed by SuriCog is the world’s first solution using the eye as a real-time mobile digital cursor, while maintaining full mobility. In order to reduce the time of eye image transmission, image compression techniques can be employed. Being hardware implemented, several standard image coding systems (JPEG and JPEG2000) were evaluated for their potential use in the next generation device of the EyeDee product line. In order to satisfy low-power, low-heat, low-MIPS requirements several non-typical approaches have been considered. One example consists in the complete replacement of currently used eye tracking algorithm based on image processing coupled with geometric eye modeling by a precisely tuned and perfectly trained neural network, which directly transforms wirelessly transmitted floating-point values of decimated eye image (result of the 3D perspective projection of a model of rotating pupil disk) into five floating-point parameters of pupil’s ellipse (result of the eye tracking). Hence implementation of the eye tracking algorithm is reduced to a known challenge of neural network construction and training, preliminary results of which are presented in the paper.

Keywords

Eye tracking Human–machine interaction Neural networks 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pavel Morozkin
    • 1
    • 2
  • Marc Swynghedauw
    • 1
  • Maria Trocan
    • 2
  1. 1.SuriCogParisFrance
  2. 2.Institut Supérieur d’Electronique de ParisParisFrance

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