Abstract
Pupillary diameter monitoring has proven successful at objectively measuring cognitive load. This work presents three robust RGB video based pupillary diameter trackers and compares them for measuring cognitive load using commodity cameras. We investigate the use of modified starburst algorithm from previous work and propose two algorithms: 2-Level Snakuscules and a convolutional neural network which we call PupilNet. Our results show PupilNet outperforms other algorithms in measuring pupil dilation, is robust to various lighting conditions, and robust to different eye colors. In addition, this work explores various deep learning architecture for extracting the pupillary response as well as understand the network's behavior with three visualization techniques: feature and filter map visualization, gradient ascent, and occluded heat map. The study shows promising results for extracting the pupillary response as objective cognitive load measurement.
Degree Date
Fall 12-16-2017
Document Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science and Engineering
Advisor
Eric C. Larson
Subject Area
Computer Science
Number of Pages
152
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Wangwiwattana, Chatchai, "RGB Image-Based Pupillary Diameter Tracking with Deep Convolutional Neural Networks" (2017). Computer Science and Engineering Theses and Dissertations. 1.
https://scholar.smu.edu/engineering_compsci_etds/1
