A Review Of Video-Based And Machine Learning Approaches To Human Eye Blink Detection In Video
Publisher
The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Abstract
Automation of detection of human eye blink in video has a broad array of applications, including detection of disease, anti-spoofing software, and helping individuals with physical disabilities interact with computers. The present work provides a review of several papers within the past two decades which propose methods for automated blink detection, highlighting the evolution of the field alongside developments in machine learning techniques. Then, the strengths and shortcomings of several popular approaches are evaluated in the context of eye blink detection. Namely, I focus on appearance-based and motion-based computation, support vector machines, convolutional neural networks, and long short-term memory networks. Finally, I report the beginnings of a reproduction of the methods outlined in one of the papers reviewed.Type
textElectronic Thesis
Degree Name
B.A.Degree Program
Honors CollegeComputer Science