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.
|Title of host publication||Unknown Host Publication|
|Number of pages||7|
|Publication status||Accepted/In press - 10 Oct 2017|
|Event||1st International Workshop on Affective Computing in Biomedicine and Healthcare (ACBH 2017) - Kansas City, MO, USA.|
Duration: 10 Oct 2017 → …
|Workshop||1st International Workshop on Affective Computing in Biomedicine and Healthcare (ACBH 2017)|
|Period||10/10/17 → …|
- Pupil Dilation
- Cognitive Workload.
Samara, A., Galway, L., Bond, R., & Wang, H. (Accepted/In press). Tracking and Evaluation of Pupil Dilation via Facial Point Marker Analysis. In Unknown Host Publication IEEE. http://uir.ulster.ac.uk/38953/1/ACBH17%20-%20Acceptance%20Notification.pdf