Tracking and Evaluation of Pupil Dilation via Facial Point Marker Analysis

Anas Samara, Leo Galway, Raymond Bond, Hui Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pupillary behavior and dilation have been considered in the literature as an effective input for the measurement of cognitive workload and stress. In this work, we explore the correlation between pupil dilation and features extracted from low quality video frames that have been captured using a normal webcam during a set of computer-based tasks. The methodology presented herein attempts to develop an alternative, cost effective technique for the representation of pupil dilation in order to track pupillary behavior from images instead of employing specialised, high-cost eye-tracking devices, which typically require specialist expertise during setup and calibration. A description of the data collection protocol and subsequent data analysis is presented. The results obtained indicate that there is a moderate correlation achieved through the use of a linear regression model, which employs fiducial point features as independent variables, and pupil size measured by an infrared-based eye-tracker as the dependent variable. Furthermore, an example of the pupil size variation within a game-based task context is shown, whereby one can easily relate the engagement and the amount of mental processing during gameplay.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM): Proceedings
Place of PublicationUnited States
Publisher IEEE
DOIs
Publication statusPublished - 18 Dec 2017

Bibliographical note

1st International Workshop on Affective Computing in Biomedicine and Healthcare (ACBH 2017) ; Conference date: 10-10-2017

Keywords

  • Eye-tracking
  • Pupil Dilation
  • Cognitive Workload.

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