Supervised Descent Method (SDM) applied to accurate pupil detection in off-the-shelf eye tracking systems


Conference Paper

© 2018 Copyright held by the owner/author(s). The precise detection of pupil/iris center is key to estimate gaze accurately. This fact becomes specially challenging in low cost frameworks in which the algorithms employed for high performance systems fail. In the last years an outstanding effort has been made in order to apply training-based methods to low resolution images. In this paper, Supervised Descent Method (SDM) is applied to GI4E database. The 2D landmarks employed for training are the corners of the eyes and the pupil centers. In order to validate the algorithm proposed, a cross validation procedure is performed. The strategy employed for the training allows us to affirm that our method can potentially outperform the state of the art algorithms applied to the same dataset in terms of 2D accuracy. The promising results encourage to carry on in the study of training-based methods for eye tracking.

Full Text

Duke Authors

Cited Authors

  • Larumbe, A; Cabeza, R; Villanueva, A

Published Date

  • June 14, 2018

Published In

  • Eye Tracking Research and Applications Symposium (Etra)

International Standard Book Number 13 (ISBN-13)

  • 9781450357067

Digital Object Identifier (DOI)

  • 10.1145/3204493.3204551

Citation Source

  • Scopus