Performance Evaluation of the Gazepoint GP3 Eye Tracking Device Based on Pupil Dilation

• Published in: Augmented Cognition. Neurocognition and Machine Learning pp. 166 - 175
• Main Authors: Mannaru, Pujitha, Balasingam, Balakumar, Pattipati, Krishna, Sibley, Ciara, Coyne, Joseph T.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Eye tracker performance; Gazepoint; Low-cost eye trackers; Memory load; Power spectral density; Pupil dilation; TEPR
• ISBN: 9783319586274; 3319586270
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-58628-1_14

Eye tracking is considered one of the most salient methods to study the cognitive demands of humans in human computer interactive systems, due to the unobtrusiveness, flexibility and the development of inexpensive eye trackers. In this work, we evaluate the applicability of these low cost eyetrackers to study pupillary response to varying memory loads and luminance conditions. Specifically, we examine a low-cost eye tracker, the Gazepoint GP3, and objectively evaluate its ability to differentiate pupil dilation metrics under different cognitive loads and luminance conditions. The classification performance is computed in the form of a receiver operating characteristic (ROC) curve and the results indicate that Gazepoint provides a reliable eye tracker to human computer interaction applications requiring pupil dilation studies.