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

Date

2018

Director

Publisher

ACM
Acceso abierto / Sarbide irekia
Contribución a congreso / Biltzarrerako ekarpena
Versión aceptada / Onetsi den bertsioa

Project identifier

  • MINECO//TIN2014-52897-R/ES/ recolecta
  • AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84388-R/ES/ recolecta
Impacto
Google Scholar
No disponible en Scopus

Abstract

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.

Description

Keywords

Eye tracking, Supervised descent method, SDM, Cascaded regressors, 2D iris center estimation

Department

Ingeniería Eléctrica, Electrónica y de Comunicación / Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren

Faculty/School

Degree

Doctorate program

item.page.cita

Andoni Larumbe, Rafael Cabeza, and Arantxa Villanueva. 2018. Supervised Descent Method (SDM) applied to accurate pupil detection in off-the-shelf eye tracking systems. In ETRA ’18: 2018 Symposium on Eye Tracking Research and Applications, June 14–17, 2018, Warsaw, Poland. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3204493.3204551

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